Dynamic evaluation method and system for electric energy quality of new energy station

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a new energy station electric energy quality dynamic evaluation method and system, and the method comprises the steps: determining the voltage change correlation between monitoring points at each moment, carrying out the extension for a plurality of times from the monitoring points in a new energy station through combining the connection condition between the monitoring points on a power grid, and constructing a local network, dynamically updating the local network according to the active power difference of the monitoring points in the local network at different moments and the magnitude of the new energy station output fluctuation of the monitoring points in the new energy station at each moment, obtaining a final local network at each moment, and in the final local network at the current moment, carrying out dynamic updating on the local network according to the active power difference of the monitoring points in the local network at different moments and the magnitude of the new energy station output fluctuation of the monitoring points in the new energy station at each moment. And determining the electric energy quality of the new energy station at the current moment. According to the method, the local network is dynamically updated in consideration of the power grid load change and meteorological condition fluctuation of the local network, so that the maximum performance of the electric energy fluctuation of the new energy station in the local network is met, and the dynamic evaluation of the electric energy quality of the new energy station is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of measuring electrical variables, and in particular to a method and system for dynamically evaluating power quality at a new energy station. Background Art

[0002] Renewable energy stations are becoming increasingly important in power systems, but due to the intermittent and fluctuating nature of their power generation, power quality issues are becoming increasingly prominent. Therefore, research on dynamic power quality assessment methods for renewable energy stations is particularly important. Power quality refers to the ability of a power system's output to meet load demands in terms of frequency, amplitude, and waveform. Renewable energy stations are subject to the influence of the natural environment. For example, photovoltaic stations are limited by light intensity, making them more susceptible to voltage and frequency fluctuations, which can affect the stability and reliability of the power system.

[0003] Existing problems: The power quality of existing new energy stations is often directly monitored by monitoring the station output. At this time, some small fluctuations are easy to be missed. After the new energy station is connected to the power grid, the station output fluctuations may be amplified within the local range of the power grid. However, the local network connected to the new energy station has dynamic fluctuation characteristics under the influence of factors such as changes in grid load and fluctuations in meteorological conditions. Therefore, a fixed local network cannot guarantee that it can continuously reflect the power quality of the new energy station. Summary of the Invention

[0004] The present invention provides a method and system for dynamically evaluating power quality of a new energy station to solve existing problems.

[0005] The present invention provides a method and system for dynamically evaluating power quality at a new energy station using the following technical solutions: An embodiment of the present invention provides a method for dynamically evaluating power quality at a new energy station, the method comprising the following steps: Obtain the rated voltage and rated frequency of each monitoring point on the power grid, as well as the voltage, frequency, and active power at each moment, and then obtain the output fluctuations of the new energy station at each monitoring point within the new energy station; According to the voltage change differences between the monitoring points at the same time, the voltage change correlation between the monitoring points at each time is determined; On the power grid, based on the voltage difference between each monitoring point at adjacent moments, the correlation of voltage changes between monitoring points at each moment, and the connectivity between monitoring points, a local network is constructed by expanding several times starting from the monitoring points within the new energy station. Based on the active power differences between monitoring points within the local network at different moments and the magnitude of the output fluctuations of the new energy station at each moment, the local network is dynamically updated to obtain the final local network at each moment. In the final local network at the current moment, the power quality of the new energy station at the current moment is determined based on the difference between the voltage and the rated voltage, the difference between the frequency and the rated frequency at each monitoring point at the current moment, as well as the voltage difference and frequency difference at adjacent moments.

[0006] Furthermore, the specific steps of determining the voltage change correlation between monitoring points at each moment include the following: Each monitoring point in The voltage at the moment minus the The voltage difference at the time is recorded as the voltage difference at each monitoring point at the Voltage change value at the moment; Preset quantity threshold , in Time to During the time period, a polynomial fitting is performed on the voltage of each monitoring point at all times to obtain the voltage of each monitoring point at the first The regression difference at the moment; According to the acquisition of any two monitoring points in The voltage change value and regression difference at the time are used to determine the voltage change value and regression difference at the time of the arbitrary two monitoring points. Voltage change correlation at each moment.

[0007] Further, the method of obtaining any two monitoring points in the The voltage change value and regression difference at the time are used to determine the voltage change value and regression difference at the time of the arbitrary two monitoring points. The voltage change correlation at each moment includes the following specific steps: Get any two monitoring points in the The absolute value of the difference between the voltage change values ​​at the time is recorded as the first difference value, and the absolute value of the voltage change value at the time of any two monitoring points is obtained. The inverse proportional normalized value of the mean of the regression difference at the time is recorded as the first similarity value, and the inverse proportional normalized value of the product of the first similarity value and the first difference value is used as the inverse proportional normalized value of the regression difference between any two monitoring points at the time Voltage change correlation at each moment.

[0008] Furthermore, the obtaining of the final local network at each moment includes the following specific steps: The monitoring points within the new energy station will be used as the benchmark monitoring points, and the monitoring points directly connected to the benchmark monitoring points will be used as monitoring points to be merged; According to the voltage difference of each monitoring point to be merged at adjacent moments, the voltage of the reference monitoring point and each monitoring point to be merged is The voltage change correlation at the moment is determined The probability of merging the benchmark monitoring point with each monitoring point to be merged at the moment; According to the merging probability, screening out the merging monitoring points; The first extended local network consisting of benchmark monitoring points and combined monitoring points; According to the merging probability and the connection between the monitoring points, a new merged monitoring point is selected from the monitoring points directly connected to the merged monitoring point; Merge the newly merged monitoring point into the first extended local network to obtain the second extended local network; Preset extension times , and so on, The second extended local network is recorded as The final local network at the moment; Preset local network update interval , every Finally, the final local network at each moment is obtained by updating and judging based on the difference in active power of the monitoring points in the final local network at different times and the fluctuation in the output of the new energy station at each moment of the monitoring points in the new energy station.

[0009] Furthermore, the voltage difference of each monitoring point to be merged at adjacent moments is combined with the reference monitoring point and each monitoring point to be merged at the first The voltage change correlation at the moment is determined The merging probability of the benchmark monitoring point and each monitoring point to be merged at the time instant includes the following specific steps: In the Time to During the time period, The monitoring points to be merged are in Moment and The absolute value of the voltage difference at the time is recorded as The voltage difference value at the time, the moment when the voltage difference value is greater than the preset fluctuation threshold is recorded as the voltage fluctuation moment; Preset time range , No. Moment and The time interval is ,and ; Get the The time interval set consisting of the time intervals of the voltage fluctuation moment and all other voltage fluctuation moments is obtained, and the maximum value of the absolute value of the autocorrelation function value of the time interval set in different lags is obtained as the first The regularity of voltage fluctuation moments; The first The product of the inverse proportional value of the regularity of the voltage fluctuation moment and the voltage difference value is recorded as the The degree of influence of voltage fluctuation on the station voltage at each moment; Obtain the inverse proportional normalized value of the impact degree on the station voltage at each voltage fluctuation moment, record it as the impact adjustment value at each voltage fluctuation moment, obtain the sum of the impact adjustment values ​​of all voltage fluctuation moments, record it as the first sum value, and compare the benchmark monitoring point with the first sum value. The monitoring points to be merged are The normalized value of the product of the voltage change correlation at the time and the first sum value is used as the first The benchmark monitoring point at the moment The merging probability of the monitoring points to be merged.

[0010] Furthermore, the steps of screening out the merged monitoring points according to the merge probability include the following specific steps: The first The monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold among the merging probabilities of the benchmark monitoring point and all the monitoring points to be merged at the time is recorded as the merged monitoring point.

[0011] Furthermore, the method of selecting a new merged monitoring point from monitoring points directly connected to the merged monitoring point according to the merging probability and the connection between the monitoring points includes the following specific steps: The merged monitoring point is used as the new benchmark monitoring point, and the monitoring points that are directly connected to the new benchmark monitoring point and are not in the first extended local network are obtained as the second monitoring points to be merged; The acquisition starts from the benchmark monitoring point, passes through only one new benchmark monitoring point, and reaches the Several propagation paths of the secondary monitoring points to be merged; In the On the propagation path, except for the All other monitoring points except the second monitoring points to be merged are recorded as reference monitoring points; Get the inverse of the number of monitoring points directly connected to each reference monitoring point, record it as the credibility of each reference monitoring point, and get the Time The normalized value of the product of the probability of merging the secondary monitoring points to be merged with each reference monitoring point and the credibility of each reference monitoring point is recorded as the merged feature value, and the first The secondary monitoring points to be merged and all reference monitoring points are in the The mean of the combined eigenvalues ​​at the moment is taken as the The merging probability on the propagation paths; The mean of the combined probabilities on all propagation paths is taken as the Time The merging probability of the secondary monitoring points to be merged; The first Among the merging probabilities of all secondary monitoring points to be merged at the time instant, the secondary monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold is recorded as the new merging monitoring point.

[0012] Furthermore, the preset local network update time interval , every Finally, based on the difference in active power at different times at the monitoring points in the final local network and the magnitude of the output fluctuation of the new energy station at each moment at the monitoring points in the new energy station, an update judgment is made to obtain the final local network at each moment. The specific steps include the following: In the Time to During the time period, obtain the maximum value of the output fluctuation of the new energy station at all times of the monitoring point in the new energy station, record it as the target output fluctuation, obtain the normalized value of the range of active power of each monitoring point at all times, record it as the power fluctuation value of each monitoring point, and obtain the target output fluctuation. The mean of the power fluctuation values ​​of all monitoring points in the final local network at the time is recorded as the first mean, and the normalized value of the product of the first mean and the target output fluctuation is recorded as the first mean. Possibility of local network updates at any time; Among them, Moment and The time interval is ,and ; When When the local network update possibility at the time is greater than the preset update threshold, The final local network acquisition method at the moment, obtain the The final local network at the moment; When When the local network update possibility at the time is less than or equal to the preset update threshold, the The final local network at the moment is The final local network at this moment.

[0013] Furthermore, the specific steps of determining the power quality of the new energy station at the current moment include: Obtain the absolute value of the difference between the voltage at each monitoring point at the current moment and the rated voltage, recorded as the second difference value; obtain the absolute value of the difference between the voltage at each monitoring point at the current moment and the adjacent previous moment, recorded as the third difference value; in the final local network at the current moment, obtain the mean of the second difference values ​​and the mean of the third difference values ​​corresponding to all monitoring points, recorded as the second mean and the third mean, respectively; and take the normalized value of the sum of the second mean and the third mean as the power quality represented by the voltage at the current moment; Obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the rated frequency, recorded as the fourth difference value; obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the adjacent previous moment, recorded as the fifth difference value; in the final local network at the current moment, obtain the mean of the fourth difference values ​​and the mean of the fifth difference values ​​corresponding to all monitoring points, recorded as the fourth mean and the fifth mean, respectively; and take the normalized value of the sum of the fourth mean and the fifth mean, recorded as the power quality represented by the frequency at the current moment; The average of the power quality represented by the voltage at the current moment and the power quality represented by the frequency at the current moment is obtained as the power quality of the new energy station at the current moment.

[0014] The present invention also proposes a dynamic evaluation system for power quality of a new energy station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program stored in the memory to implement the steps of the aforementioned method for dynamic evaluation of power quality of a new energy station.

[0015] The beneficial effects of the technical solution of the present invention are as follows: by connecting the new energy station to the power grid, then setting up monitoring points in the power grid, using the monitoring points to amplify the power fluctuations of the new energy station, and realizing the monitoring of small fluctuations in the station, and at the same time combining the dynamic change consistency relationship between the new energy station and other monitoring points, a local network of the new energy station is constructed, and the power fluctuations of the new energy station are more accurately amplified in the local network. Furthermore, the present invention takes into account the changes in the local network load and the fluctuations in meteorological conditions, and dynamically updates the local network to meet the maximum performance of the power fluctuations of the new energy station in the local network, thereby realizing dynamic evaluation of the power quality of the new energy station. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flowchart of the steps of a method for dynamic evaluation of power quality of a new energy station according to the present invention; Figure 2 A schematic diagram of the graph structure. DETAILED DESCRIPTION

[0018] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for dynamically assessing power quality at a new energy station, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the method and system for dynamic evaluation of power quality of a new energy station provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flowchart of a method for dynamic power quality assessment of a new energy station provided by an embodiment of the present invention, the method comprising the following steps: Step S001: Obtain the rated voltage, rated frequency, voltage, frequency, and active power of each monitoring point on the power grid at each moment, and then obtain the output fluctuation of the new energy station at each monitoring point in the new energy station at each moment.

[0022] It should be noted that photovoltaic (PV) sites (abbreviated as "PV sites") are power generation facilities that utilize the solar photovoltaic effect to convert light energy into electricity. Their core function is to convert intermittent solar energy into stable electricity and connect it to the grid. They are a key form of renewable energy generation. The electricity they generate generally needs to be connected to the grid for transmission and use. Therefore, when evaluating their power quality, monitoring points must be deployed within the connected grid. The specific monitoring points must cover key nodes where the PV site interacts with the grid to ensure comprehensive capture of power quality issues. These primarily include the site side, the grid-connected side, and the load side. Therefore, monitoring points are primarily located at the site's inverter exit point, the grid-connected point, and at bifurcations in the grid-connected line. Bifurcations are intersections within the site's grid-connected point that are less than 50 km away.

[0023] To ensure the accuracy and effectiveness of monitoring data, data collection must meet the following requirements: (1) Monitoring parameter requirements: Core power quality indicators must be covered: voltage, frequency, current, active power, reactive power, total harmonic distortion (THD), harmonic content, voltage fluctuation and flicker.

[0024] (2) Equipment and parameters: The core equipment for power quality monitoring at photovoltaic stations is the power quality monitor (PQM), also known as a "power quality analyzer." The parameters of the PQM must cover sampling accuracy, measurement range, and functional adaptability. Specific requirements are as follows: Finally, the collected data is transmitted to the evaluation center, which caches the data and processes it. The evaluation center is connected to the site control center and can read site data, including photovoltaic angle, temperature, light intensity, etc.

[0025] In this way, the rated voltage and rated frequency of each monitoring point on the power grid, as well as the voltage, frequency, and active power at each moment can be obtained. There is a monitoring point within the new energy station on the power grid (that is, the inverter outlet monitoring point within the new energy station), and the output fluctuation of the new energy station at each moment of the monitoring point within the new energy station can be obtained.

[0026] It should be noted that in this embodiment, the acquisition of the output fluctuation of the new energy station is a well-known technology, and the specific method will not be introduced here.

[0027] Step S002: determining the voltage change correlation between the monitoring points at each moment according to the voltage change differences between the monitoring points at the same moment.

[0028] What needs to be explained is that the current assessment of the power quality in the station requires the station to be connected to the power grid, and then monitoring is performed at different locations in the power grid to accurately reflect the power quality of the station. When using different monitoring points to reflect the power quality of the station, it is first necessary to ensure that the different monitoring points in the power grid have consistent fluctuations with the monitoring points in the station, that is, there needs to be a correlation between the monitoring points. At each sampling moment, each monitoring point corresponds to a voltage data. At this time, the correlation of changes between the monitoring points can be expressed by the correlation of voltage changes. The correlation of voltages between monitoring points is expressed as the consistency of voltage changes, and the deviation of the corresponding monitoring point itself at a single moment needs to be considered. The larger the deviation itself, the less its voltage change can reflect the correlation of voltage changes between monitoring points.

[0029] Each monitoring point in The voltage at the moment minus the The voltage difference at the time is recorded as the voltage difference at each monitoring point at the The voltage change value at the time.

[0030] The voltage change value at the first moment is not analyzed.

[0031] Preset quantity threshold The value is 9, and this is used as an example for description.

[0032] In the Time to During the time period, a polynomial fitting is performed on the voltage of each monitoring point at all times to obtain the voltage of each monitoring point at the first The regression difference at the moment.

[0033] The polynomial fitting uses the least squares method, which is a well-known technique and will not be described in detail here. The larger the regression error, the greater the difference between the fitted value and the actual value.

[0034] Get any two monitoring points in the The absolute value of the difference between the voltage change values ​​at the time is recorded as the first difference value, and the absolute value of the voltage change value at the time of any two monitoring points is obtained. The mean of the regression difference at time The inverse proportional normalized value of is recorded as the first similarity value, and the product of the first similarity value and the first difference value is The inverse proportional normalized value of any two monitoring points is Voltage change correlation at each moment.

[0035] It should be noted that: in this embodiment, and As and The inverse normalized value of is a linear normalization function used to normalize data values ​​to between 0 and 1. The smaller the regression difference, the more credible the first difference value is. Therefore, the first difference value is adjusted with the first similarity value. When the first difference value is larger, it means that any two monitoring points are in the first The more dissimilar the voltage changes at the time, the more similar the two monitoring points are. Voltage change correlation at each moment.

[0036] Step S003: On the power grid, based on the voltage difference of each monitoring point at adjacent moments, the correlation of voltage changes between the monitoring points at each moment, and the connection status between the monitoring points, a local network is constructed by several expansions starting from the monitoring points in the new energy station. Based on the active power difference of the monitoring points in the local network at different moments and the magnitude of the output fluctuation of the new energy station at each moment of the monitoring points in the new energy station, the local network is dynamically updated to obtain the final local network at each moment.

[0037] It should be noted that the primary purpose of constructing a local network based on the current station is to utilize it to demonstrate the station's power quality. Therefore, the monitoring points within the local network and the relationships between them must reflect the station's power fluctuations. Therefore, the current local network construction is primarily based on the consistent relationship between the changes in monitoring points within the station and other monitoring points. Starting from a monitoring point within the station, other monitoring points are searched and merged to form the final local network. The key to constructing a local network at this point lies in merging monitoring points, starting with the monitoring points within the station and then merging other monitoring points.

[0038] It's important to further clarify that for the local network to be constructed, we need to start from a single monitoring point, then search for adjacent monitoring points based on their connectivity. Then, using the correlation between adjacent monitoring points, we determine the monitoring points to merge. Since the local network is used to reflect the power quality of the station, we need to start from the monitoring points within the station, namely the inverter outlet monitoring points within the station, to determine the monitoring points to be merged.

[0039] Among all monitoring points on the power grid, the monitoring points are connected through line connections to form a graph structure, which is a well-known operation.

[0040] Among them, the graph structure is an undirected graph, and the graph structure diagram is as follows: Figure 2 As shown, Figure 2 There are monitoring points 0, 1, 2, 3, and 4 in the network, and the side length between the monitoring points is the line length between the monitoring points.

[0041] In the graph structure, the monitoring points within the new energy station are used as the benchmark monitoring points, and the monitoring points directly connected to the benchmark monitoring points are used as monitoring points to be merged.

[0042] It's important to note that after identifying the monitoring points to be merged, the corresponding merging probabilities need to be determined to determine the monitoring points to be merged. The merged monitoring points must exhibit similar fluctuation patterns to the baseline monitoring points. Fluctuations in grid-side loads (such as industrial motor startups and shutdowns, and peaks and valleys in residential electricity consumption) can overlap or offset fluctuations in renewable energy output. Therefore, the smaller the voltage fluctuations at the monitoring point to be merged, the less effective it is at covering the current voltage fluctuations within the station, and the greater the probability of merging. Furthermore, different loads consume electricity at different times, and the output fluctuations of current photovoltaic stations also exhibit certain temporal patterns, such as distinct fluctuation trends in output during the morning, afternoon, and evening hours. Therefore, the more regular the voltage fluctuations at the monitoring point to be merged, the less effective it is at covering the current voltage fluctuations within the station, and the greater the probability of merging.

[0043] Preset time range The period is 10 days, and the preset fluctuation threshold is 3. This example is used for description.

[0044] In the Time to During the time period, The monitoring points to be merged are in Moment and The absolute value of the voltage difference at the time is recorded as The monitoring points to be merged are in The voltage difference value at the time is recorded as the voltage fluctuation time, and the time when the voltage difference value is greater than the preset fluctuation threshold is recorded as the voltage fluctuation time.

[0045] Among them, Time to The voltage change value at the first moment in the time period is not analyzed. Moment and The time interval is ,and .

[0046] Get the The time interval set consisting of the time intervals of the voltage fluctuation moment and all other voltage fluctuation moments is obtained, and the maximum value of the absolute value of the autocorrelation function value of the time interval set in different lags is obtained as the first The regularity of the voltage fluctuation moments.

[0047] It should be noted that obtaining the autocorrelation function values ​​for a set of time intervals at different lags is a well-known operation, and the specific method is not described here. When a fluctuating voltage exhibits regularity at the moment of occurrence, the fluctuating voltage is easily identified, and thus is less likely to "superimpose" or "cancel" with station voltage fluctuations, indicating a low impact on the station voltage.

[0048] The first The inverse proportional value of the regularity of the voltage fluctuation moment is The product of the voltage difference values ​​at the moment of voltage fluctuation is recorded as The degree of impact of voltage fluctuation on the station voltage at each moment.

[0049] Since the autocorrelation function value ranges from -1 to 1, the regularity is between 0 and 1. Therefore, in this embodiment, the difference between 1 and the regularity is used as the inverse proportional value of the regularity.

[0050] Obtain the impact of each voltage fluctuation on the station voltage The inverse proportional normalized value is recorded as the impact adjustment value of each voltage fluctuation moment, and the sum of the impact adjustment values ​​of all voltage fluctuation moments is obtained and recorded as the first sum value. The monitoring points to be merged are in The product of the voltage change correlation at the time and the first sum value The normalized value of The benchmark monitoring point at the moment The merging probability of the monitoring points to be merged.

[0051] It should be noted that: in this embodiment, As The inverse normalized value of As The normalized value of . When the voltage fluctuation of the monitoring point to be merged has a smaller impact on the station voltage, that is, the larger the first sum value, the more reliable the voltage change correlation between the reference monitoring point and the monitoring point to be merged is. Therefore, the first sum value is used to adjust the voltage change correlation to obtain the merger probability.

[0052] The preset merging threshold is 0.7, which is used as an example for description.

[0053] The first The monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold among the merging probabilities of the benchmark monitoring point and all the monitoring points to be merged at the time is recorded as the merged monitoring point.

[0054] In the graph structure, the first extended local network consisting of the benchmark monitoring point and all merged monitoring points is obtained.

[0055] It should be noted that the above merging process only begins with monitoring points within the station. The initial local network formed by merging adjacent monitoring points is the first extended local network. Because voltage fluctuations within the station are transmitted not only to directly connected monitoring points in the graph structure, but also to other indirectly connected monitoring points in the graph structure, it is necessary to continue merging additional monitoring points based on the initial local network, i.e., continuously merging monitoring points.

[0056] The merged monitoring point is used as the new benchmark monitoring point. In the graph structure, the monitoring points directly connected to the new benchmark monitoring point are obtained as the second monitoring points to be merged. Among them, the second monitoring points to be merged are not in the first extended local network.

[0057] It should be noted that a secondary monitoring point to be merged may correspond to multiple new benchmark monitoring points, so there is more than one propagation path between it and the monitoring points within the station. Therefore, the multiple propagation paths from the secondary monitoring point to be merged to the monitoring points within the station are first determined.

[0058] For the Secondary monitoring points to be merged, in the graph structure, starting from the base monitoring point, only passing through a new base monitoring point, to reach the Several propagation paths of the secondary monitoring points to be merged.

[0059] In the On the propagation path, except for the All other monitoring points except the secondary monitoring points to be merged are recorded as reference monitoring points.

[0060] According to the The benchmark monitoring point at the moment The acquisition method of the merging probability of the monitoring points to be merged is to obtain the Time The merging probability of the secondary monitoring points to be merged and each reference monitoring point.

[0061] In the graph structure, the inverse of the number of monitoring points directly connected to each reference monitoring point is obtained, which is recorded as the credibility of each reference monitoring point. Time The product of the probability of merging the secondary monitoring points to be merged with each reference monitoring point and the reliability of each reference monitoring point The normalized value of The secondary monitoring points to be merged and each reference monitoring point in the The combined eigenvalue at the moment is obtained The secondary monitoring points to be merged and all reference monitoring points are in the The mean of the combined eigenvalues ​​at the moment is taken as the The merging probability on the propagation paths.

[0062] The mean of the combined probabilities on all propagation paths is taken as the Time The merging probability of the secondary monitoring points to be merged.

[0063] It should be noted that the more monitoring points a monitoring point to be merged is directly connected to in a propagation path, the more complex the voltage fluctuation sources of the monitoring points passed through are, and the less credible the merging probability calculated between them and the monitoring point to be merged is. Therefore, the reciprocal of the number of monitoring points directly connected to each reference monitoring point is used as the credibility of each reference monitoring point to adjust the merging probability and obtain the merging probability on each propagation path. As The normalized value of .

[0064] The first Among the merging probabilities of all secondary monitoring points to be merged at the time instant, the secondary monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold is recorded as the new merging monitoring point.

[0065] In the graph structure, the newly merged monitoring points are merged into the first extended local network to obtain the second extended local network.

[0066] Preset extension times The value is 7, and this is used as an example for description.

[0067] By analogy, the The second extended local network is recorded as The final local network at this moment.

[0068] What needs to be explained is: take the acquisition process of the third extended local network as an example, that is, take the newly merged monitoring point as the updated benchmark monitoring point, and obtain the monitoring point directly connected to the updated benchmark monitoring point in the graph structure as the monitoring point to be merged three times. Among them, the monitoring point to be merged three times is not in the second extended local network. For any monitoring point to be merged three times, in the graph structure, obtain several propagation paths starting from the benchmark monitoring point, passing through a new benchmark monitoring point, then passing through an updated benchmark monitoring point, and finally reaching the arbitrary monitoring point to be merged three times. Then obtain the merging probability on each propagation path, so as to obtain the first The merging probability of each of the three monitoring points to be merged at the time is used to select the latest merged monitoring point.

[0069] It should be further explained that a local network has been constructed to represent the current fluctuations in the power supply of a renewable energy station. However, the current renewable energy station is a photovoltaic power generation station, and its output is directly affected by sunlight intensity. In actual operation, light intensity varies significantly throughout the day, leading to fluctuations in output. Even if energy storage is implemented within the station, its regulation capacity is limited. Coupled with sudden weather changes, output fluctuations are inevitable. Furthermore, the local network may have peak and undershoot periods on the load side, and different types of loads have different peak periods, such as those for residential and industrial use. Fluctuations in load consumption also affect local network fluctuations. Therefore, the relationship between the aforementioned monitoring points and those within the station varies at different times. Therefore, to ensure dynamic identification of station voltage fluctuations and dynamic assessment of station power quality, it is necessary to construct different local networks at different times, requiring dynamic updates of the local networks.

[0070] Preset local network update interval The default update threshold is 0.6, which is used as an example for description.

[0071] In the Time to During the time period, obtain the maximum value of the output fluctuation of the new energy station at all times of the monitoring point in the new energy station, record it as the target output fluctuation, and obtain the range of active power of each monitoring point at all times The normalized value of is recorded as the power fluctuation value of each monitoring point, and the The mean of the power fluctuation values ​​of all monitoring points in the final local network at the time is recorded as the first mean, and the product of the first mean and the target output fluctuation is The normalized value of Local network update possibility at any time.

[0072] When When the local network update possibility at the time is greater than the preset update threshold, The final local network acquisition method at the moment, obtain the The final local network at this moment.

[0073] When When the local network update possibility at the time is less than or equal to the preset update threshold, the The final local network at the moment is The final local network at this moment.

[0074] It should be noted that: in this embodiment, and As and The normalized value of Moment and The time interval is ,and . Order No. Time to The final local network at each moment in the time period is The final local network at time The final local network at the time and every time within 30 minutes thereafter is the first The final local network at the moment is obtained by analogy, and the final local network in real time is obtained. Thus, the dynamic update of the local network is completed.

[0075] Step S004: In the final local network at the current moment, the power quality of the new energy station at the current moment is determined based on the difference between the voltage and the rated voltage, the difference between the frequency and the rated frequency at each monitoring point at the current moment, and the voltage difference and frequency difference at adjacent moments.

[0076] It should be noted that based on the above operations, a dynamic local network was constructed within the power grid to which the current new energy station is connected. Within this constructed local network, a dynamic assessment of the station's power quality is achieved based on the dynamic fluctuations of all monitoring points. Because the current station's power quality requires dynamic assessment, the designed parameters use real-time data as much as possible. Real-time changes in voltage can directly reflect the station's power quality. The greater the voltage deviation and dynamic voltage changes at a single monitoring point, the worse the station's power quality. Furthermore, the greater the deviation and dynamic changes in the monitoring point's frequency, the worse the station's power quality.

[0077] Obtain the absolute value of the difference between the voltage of each monitoring point at the current moment and the rated voltage, recorded as the second difference value, obtain the absolute value of the difference between the voltage of each monitoring point at the current moment and the adjacent previous moment, recorded as the third difference value, in the final local network at the current moment, obtain the mean of the second difference values ​​corresponding to all monitoring points, recorded as the second mean, obtain the mean of the third difference values ​​corresponding to all monitoring points, recorded as the third mean, and sum the second mean and the third mean The normalized value of is recorded as the power quality represented by the voltage at the current moment.

[0078] Obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the rated frequency, recorded as the fourth difference value, obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the adjacent previous moment, recorded as the fifth difference value, in the final local network at the current moment, obtain the mean of the fourth difference values ​​corresponding to all monitoring points, recorded as the fourth mean, obtain the mean of the fifth difference values ​​corresponding to all monitoring points, recorded as the fifth mean, and sum the fourth mean and the fifth mean The normalized value of is recorded as the power quality represented by the frequency at the current moment.

[0079] The average of the power quality represented by the voltage at the current moment and the power quality represented by the frequency at the current moment is obtained as the power quality of the new energy station at the current moment.

[0080] It should be noted that: in this embodiment, and As and Normalized value of . Preset dynamic evaluation time interval For example, the power quality of the renewable energy station is evaluated every 5 seconds, thereby achieving dynamic evaluation of the power quality of the renewable energy station.

[0081] The present invention also provides a dynamic evaluation system for power quality of a new energy station, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program stored in the memory to implement the steps of the aforementioned method for dynamic evaluation of power quality of a new energy station.

[0082] So far, the present invention is completed.

[0083] In summary, in an embodiment of the present invention, the voltage change correlation between monitoring points at each moment is determined. On the power grid, based on the voltage difference between each monitoring point at adjacent moments, the voltage change correlation between monitoring points at each moment, and the connection between monitoring points, a local network is constructed by several expansions starting from the monitoring points within the new energy station. The local network is dynamically updated based on the active power difference of the monitoring points within the local network at different moments and the magnitude of the output fluctuation of the new energy station at each moment of the monitoring points within the new energy station. The final local network at each moment is obtained, and the power quality of the new energy station at the current moment is determined in the final local network at the current moment. The present invention takes into account the changes in the load of the local network and the fluctuations in meteorological conditions, and dynamically updates the local network to meet the maximum performance of the power fluctuation of the new energy station in the local network, thereby realizing dynamic evaluation of the power quality of the new energy station.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamic evaluation of power quality at a new energy station, characterized in that: The method comprises the following steps: Obtain the rated voltage and rated frequency of each monitoring point on the power grid, as well as the voltage, frequency, and active power at each moment, and then obtain the output fluctuations of the new energy station at each monitoring point within the new energy station; According to the voltage change differences between the monitoring points at the same time, the voltage change correlation between the monitoring points at each time is determined; On the power grid, based on the voltage difference between each monitoring point at adjacent moments, the correlation of voltage changes between monitoring points at each moment, and the connectivity between monitoring points, a local network is constructed by expanding several times starting from the monitoring points within the new energy station. Based on the active power differences between monitoring points within the local network at different moments and the magnitude of the output fluctuations of the new energy station at each moment, the local network is dynamically updated to obtain the final local network at each moment. In the final local network at the current moment, the power quality of the new energy station at the current moment is determined based on the difference between the voltage and the rated voltage, the difference between the frequency and the rated frequency at each monitoring point at the current moment, as well as the voltage difference and frequency difference at adjacent moments.

2. According to claim 1, a new energy station power quality dynamic assessment method is characterized in that: The specific steps of determining the voltage change correlation between monitoring points at each moment are as follows: Each monitoring point in The voltage at the moment minus the The voltage difference at the time is recorded as the voltage difference at each monitoring point at the Voltage change value at the moment; Preset quantity threshold , in Time to During the time period, a polynomial fitting is performed on the voltage of each monitoring point at all times to obtain the voltage of each monitoring point at the first The regression difference at the moment; According to the acquisition of any two monitoring points in The voltage change value and regression difference at the time are used to determine the voltage change value and regression difference at the time of the arbitrary two monitoring points. Voltage change correlation at each moment.

3. The method for dynamic evaluation of power quality of a new energy station according to claim 2 is characterized in that: According to obtaining any two monitoring points in The voltage change value and regression difference at the time are used to determine the voltage change value and regression difference at the time of the arbitrary two monitoring points. The voltage change correlation at each moment includes the following specific steps: Get any two monitoring points in the The absolute value of the difference between the voltage change values ​​at the time is recorded as the first difference value, and the absolute value of the voltage change value at the time of any two monitoring points is obtained. The inverse proportional normalized value of the mean of the regression difference at the time is recorded as the first similarity value, and the inverse proportional normalized value of the product of the first similarity value and the first difference value is used as the inverse proportional normalized value of the regression difference between any two monitoring points at the time Voltage change correlation at each moment.

4. The method for dynamic evaluation of power quality of a new energy station according to claim 1, characterized in that: The specific steps of obtaining the final local network at each moment are as follows: The monitoring points within the new energy station will be used as the benchmark monitoring points, and the monitoring points directly connected to the benchmark monitoring points will be used as monitoring points to be merged; According to the voltage difference of each monitoring point to be merged at adjacent moments, the voltage of the reference monitoring point and each monitoring point to be merged is The voltage change correlation at the moment is determined The probability of merging the benchmark monitoring point with each monitoring point to be merged at the moment; According to the merging probability, screening out the merging monitoring points; The first extended local network consisting of benchmark monitoring points and combined monitoring points; According to the merging probability and the connection between the monitoring points, a new merged monitoring point is selected from the monitoring points directly connected to the merged monitoring point; Merge the newly merged monitoring point into the first extended local network to obtain the second extended local network; Preset extension times , and so on, The second extended local network is recorded as The final local network at the moment; Preset local network update interval , every Finally, the final local network at each moment is obtained by updating and judging based on the difference in active power of the monitoring points in the final local network at different times and the fluctuation in the output of the new energy station at each moment of the monitoring points in the new energy station.

5. A method for dynamic evaluation of power quality of a new energy station according to claim 4, characterized in that: The voltage difference of each monitoring point to be merged at adjacent moments is combined with the reference monitoring point and each monitoring point to be merged at the first The voltage change correlation at the moment is determined The merging probability of the benchmark monitoring point and each monitoring point to be merged at the time instant includes the following specific steps: In the Time to During the time period, The monitoring points to be merged are Moment and The absolute value of the voltage difference at the time is recorded as The voltage difference value at the time, the moment when the voltage difference value is greater than the preset fluctuation threshold is recorded as the voltage fluctuation moment; Preset time range , No. Moment and The time interval is ,and ; Get the The time interval set consisting of the time intervals of the voltage fluctuation moment and all other voltage fluctuation moments is obtained, and the maximum value of the absolute value of the autocorrelation function value of the time interval set in different lags is obtained as the first The regularity of voltage fluctuation moments; The first The product of the inverse proportional value of the regularity of the voltage fluctuation moment and the voltage difference value is recorded as the The degree of influence of voltage fluctuation on the station voltage at each moment; Obtain the inverse proportional normalized value of the impact degree on the station voltage at each voltage fluctuation moment, record it as the impact adjustment value at each voltage fluctuation moment, obtain the sum of the impact adjustment values ​​of all voltage fluctuation moments, record it as the first sum value, and compare the benchmark monitoring point with the first sum value. The monitoring points to be merged are The normalized value of the product of the voltage change correlation at the time and the first sum value is used as the first The benchmark monitoring point at the moment The merging probability of the monitoring points to be merged.

6. A method for dynamic evaluation of power quality of a new energy station according to claim 4, characterized in that: The specific steps of screening out the merged monitoring points according to the merge probability are as follows: The first The monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold among the merging probabilities of the benchmark monitoring point and all the monitoring points to be merged at the time is recorded as the merged monitoring point.

7. A method for dynamic evaluation of power quality of a new energy station according to claim 4, characterized in that: The specific steps of selecting a new merged monitoring point from monitoring points directly connected to the merged monitoring point according to the merging probability and the connection between the monitoring points are as follows: The merged monitoring point is used as the new benchmark monitoring point, and the monitoring points that are directly connected to the new benchmark monitoring point and are not in the first extended local network are obtained as the second monitoring points to be merged; The acquisition starts from the benchmark monitoring point, passes through only one new benchmark monitoring point, and reaches the Several propagation paths of the secondary monitoring points to be merged; In the On the propagation path, except for the All other monitoring points except the second monitoring points to be merged are recorded as reference monitoring points; Get the inverse of the number of monitoring points directly connected to each reference monitoring point, record it as the credibility of each reference monitoring point, and get the Time The normalized value of the product of the probability of merging the secondary monitoring points to be merged with each reference monitoring point and the credibility of each reference monitoring point is recorded as the merged feature value, and the first The secondary monitoring points to be merged and all reference monitoring points are in the The mean of the combined eigenvalues ​​at the moment is taken as the The merging probability on the propagation paths; The mean of the combined probabilities on all propagation paths is taken as the Time The merging probability of the secondary monitoring points to be merged; The first Among the merging probabilities of all secondary monitoring points to be merged at the time instant, the secondary monitoring point to be merged corresponding to the merging probability greater than the preset merging threshold is recorded as the new merging monitoring point.

8. A method for dynamic evaluation of power quality of a new energy station according to claim 4, characterized in that: The preset local network update time interval , every Finally, based on the difference in active power at different times at the monitoring points in the final local network and the magnitude of the output fluctuation of the new energy station at each moment at the monitoring points in the new energy station, an update judgment is made to obtain the final local network at each moment. The specific steps include the following: In the Time to During the time period, obtain the maximum value of the output fluctuation of the new energy station at all times of the monitoring point in the new energy station, record it as the target output fluctuation, obtain the normalized value of the range of active power of each monitoring point at all times, record it as the power fluctuation value of each monitoring point, and obtain the target output fluctuation. The mean of the power fluctuation values ​​of all monitoring points in the final local network at the time is recorded as the first mean, and the normalized value of the product of the first mean and the target output fluctuation is recorded as the first mean. Possibility of local network updates at any time; Among them, Moment and The time interval is ,and ; When When the local network update possibility at the time is greater than the preset update threshold, The final local network acquisition method at the moment, obtain the The final local network at the moment; When When the local network update possibility at the time is less than or equal to the preset update threshold, the The final local network at the moment is The final local network at this moment.

9. A method for dynamic evaluation of power quality of a new energy station according to claim 1, characterized in that: The specific steps of determining the power quality of the new energy station at the current moment are as follows: Obtain the absolute value of the difference between the voltage at each monitoring point at the current moment and the rated voltage, recorded as the second difference value; obtain the absolute value of the difference between the voltage at each monitoring point at the current moment and the adjacent previous moment, recorded as the third difference value; in the final local network at the current moment, obtain the mean of the second difference values ​​and the mean of the third difference values ​​corresponding to all monitoring points, recorded as the second mean and the third mean, respectively; and take the normalized value of the sum of the second mean and the third mean as the power quality represented by the voltage at the current moment; Obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the rated frequency, recorded as the fourth difference value; obtain the absolute value of the difference between the frequency of each monitoring point at the current moment and the adjacent previous moment, recorded as the fifth difference value; in the final local network at the current moment, obtain the mean of the fourth difference values ​​and the mean of the fifth difference values ​​corresponding to all monitoring points, recorded as the fourth mean and the fifth mean, respectively; and take the normalized value of the sum of the fourth mean and the fifth mean, recorded as the power quality represented by the frequency at the current moment; The average of the power quality represented by the voltage at the current moment and the power quality represented by the frequency at the current moment is obtained as the power quality of the new energy station at the current moment.

10. A new energy station power quality dynamic assessment system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamic evaluation of power quality of a new energy station as described in any one of claims 1 to 9 are implemented.

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