Comprehensive load characteristic analysis method and system for power distribution network containing distributed power supply

Abstract

The invention discloses a comprehensive load characteristic analysis method and system for a power distribution network containing distributed power supplies, and relates to the technical field of power distribution network analysis. Stability analysis is carried out on each accessed distributed power supply regularly; the method comprises the following steps: firstly, judging whether the stored electric quantity of the distributed power supply meets the use requirement of a power distribution network, deleting the distributed power supply which does not meet the use requirement from a power supply list, completing one-time deletion processing on the power supply list, carrying out backtracking analysis on the stability of the residual distributed power supply, and deleting the distributed power supply which is unqualified in stability backtracking analysis from the power supply list. And completing secondary deletion processing on the power supply list, and dynamically adjusting the output electric quantity of each distributed power supply according to a stability backtracking analysis result. And the analysis system dynamically adjusts the output of the distributed power supply according to the backtracking analysis result, so that the output is matched with the load demand of the power distribution network, and the operation stability and safety of the power distribution network when the load is relatively large are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network analysis, and in particular to a method and system for analyzing comprehensive load characteristics of a distribution network containing distributed power sources. Background Art

[0002] The analysis system is mainly used to study and analyze the changes in the comprehensive load characteristics of the distribution network after the access of distributed power sources. With the rapid development of renewable energy (such as photovoltaics, wind power) and distributed power generation technology, a large number of distributed power sources are connected to the distribution network, which has changed the operation mode of the traditional distribution network with centralized power sources as the core. In order to improve the operation efficiency, safety and stability of the power grid, it is particularly important to conduct in-depth analysis of the load characteristics of the distribution network containing distributed power sources.

[0003] The prior art has the following defects:

[0004] 1. The existing technology lacks a comprehensive dynamic evaluation of the operating status of distributed power sources after they are connected to the distribution network. Especially when the grid load fluctuates drastically or the output of distributed power sources changes, it is impossible to obtain the power supply stability or energy storage status in real time, which easily leads to unstable power sources participating in power supply and reduces the reliability of the grid.

[0005] 2. Existing distributed power dispatch strategies are mostly based on fixed rules, which fail to dynamically respond to the real-time operating status of the distribution network and the stability analysis results of distributed power. The dispatch scheme lacks flexibility, resulting in low utilization efficiency of dispatch resources and high operating costs. Summary of the invention

[0006] The purpose of the present invention is to provide a comprehensive load characteristic analysis method and system for a distribution network containing distributed power sources, and dynamically adjust the output of the distributed power sources according to the retrospective analysis results to match the load demand of the distribution network, thereby ensuring the stability and safety of the distribution network when the load is large.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources, the method comprising the following steps:

[0008] The analysis system obtains information about all distributed power sources connected to the distribution network through the distribution network management system, initially marks each distributed power source, and constructs a power source list for all marked distributed power sources;

[0009] Perform stability analysis on each connected distributed power source regularly. When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power source meets the use of the distribution network, delete the distributed power source that does not meet the requirements from the power source list, and complete the deletion of the power source list.

[0010] Perform a back-analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability back-analysis from the power source list, complete the secondary deletion of the power source list, and dynamically adjust the output power of each distributed power source according to the stability back-analysis results;

[0011] After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result.

[0012] In a preferred embodiment, stability analysis is performed on each connected distributed power source at regular intervals, including the following steps:

[0013] After obtaining the operating data of the power generation equipment of each distributed power source, the equipment operating data is substituted into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of each distributed power source, and the expression is: In the formula, abnormal is the abnormal index, is the operating data of each power generation equipment of the distributed power source, {G1, G2, ..., G n} is the regression coefficient, and the regression coefficient is greater than 0;

[0014] The obtained abnormal index is compared with the preset index threshold. The index threshold is used to judge whether the stability of the distributed power supply is good or poor. If the abnormal index is greater than the index threshold, the stability of the distributed power supply is judged to be poor. If the abnormal index is less than or equal to the index threshold, the stability of the distributed power supply is judged to be good.

[0015] In a preferred embodiment, a backtracking analysis is performed on the stability of the remaining distributed power sources, and the distributed power sources that fail the stability backtracking analysis are deleted from the power source list, and a secondary deletion process of the power source list is completed, including the following steps:

[0016] A retrospective analysis is performed on the stability of the distributed power sources in the power source list after a deletion. The logic of the retrospective analysis is: obtain the abnormal index obtained at multiple time points in the history of the distributed power source, calculate the mean of the abnormal index and the standard deviation of the abnormal index based on the abnormal index obtained at multiple time points, and obtain the overall abnormal coefficient of the distributed power source by dividing the obtained mean of the abnormal index by the standard deviation of the abnormal index;

[0017] The obtained overall abnormality coefficient is compared with the preset second coefficient threshold. If the overall abnormality coefficient of the distributed power source is greater than the second coefficient threshold, the distributed power source stability retrospective analysis is judged to be unqualified and the distributed power source is deleted from the power source list for the second time.

[0018] In a preferred embodiment, the mean value and standard deviation of the abnormal index are calculated based on the abnormal indexes obtained at multiple time points, and the expression is: In the formula, abnormal avg is the mean of the abnormal index, abnormal Q is the standard deviation of the abnormal index, m is the number of time points, abnormal i The abnormality index value obtained at the i-th time point.

[0019] In a preferred embodiment, dynamically adjusting the output power of each distributed power source according to the stability backtracking analysis result includes the following steps:

[0020] The output power of each distributed power source is dynamically adjusted by obtaining the overall abnormal coefficient. The adjustment algorithm is: new =dl old / ln(yc z +1), where dl new is the output power after adjustment, dl old is the initial preset output power, yc z is the overall abnormal coefficient.

[0021] In a preferred embodiment, after periodically obtaining the backtracking analysis results and the usage status of the distributed power source, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result, including the following steps:

[0022] Regularly obtain the overall abnormal coefficient of distributed power sources, the usage frequency of distributed power sources, and the accumulated idle time;

[0023] The overall abnormal coefficient, usage frequency and accumulated idle time are calculated comprehensively to obtain the management factor of distributed power supply, and the expression is: In the formula, GL z is the management factor, δ is the usage frequency, yc z is the overall abnormality coefficient, ε is the accumulated idle time, α, β, and γ are the adjustment coefficients of the usage frequency, the overall abnormality coefficient, and the accumulated idle time, and α, β, and γ are all greater than 0;

[0024] The obtained management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is less than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is greater than the management threshold, it is determined that the distribution network does not need to retain the distributed power source.

[0025] The management strategy generated based on the judgment result is: the distributed power sources that need to be retained are classified into the retention set, and the distributed power sources that do not need to be retained are classified into the deletion set, and the retention set and the deletion set are sent to the distribution network management platform.

[0026] In a preferred embodiment, the distributed power source is a photovoltaic power station, and the power generation equipment operation data of the photovoltaic power station is obtained including the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation amplitude, the boost transformer partial discharge intensity and the light intensity fluctuation;

[0027] Substitute the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation amplitude, the step-up transformer partial discharge intensity and the light intensity fluctuation into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of the photovoltaic power station. The expression is: In the formula, abnormal is the abnormal index, They are the temperature rise rate of the photovoltaic panel, the power factor deviation amplitude, the partial discharge intensity of the step-up transformer and the light intensity fluctuation. {G1, G2, G3, G4} are the regression coefficients, and the regression coefficients are greater than 0.

[0028] A comprehensive load characteristic analysis system for a distribution network containing distributed power sources, including a power list building module, a distributed power source analysis module, and a management module;

[0029] Power list building module: obtains all distributed power information connected to the distribution network through the distribution network management system, initially marks each distributed power source, and builds a power list for all marked distributed power sources;

[0030] Distributed power analysis module: regularly conducts stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power source meets the use of the distribution network, delete the distributed power sources that do not meet the requirements from the power source list, complete the first deletion of the power source list, perform a retrospective analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability retrospective analysis from the power source list, complete the second deletion of the power source list, and dynamically adjust the output power of each distributed power source according to the stability retrospective analysis results;

[0031] Management module: After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it determines whether the distribution network still needs to use the distributed power source, and generates a management strategy based on the judgment results.

[0032] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0033] 1. The present invention periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the stored power of the distributed power source meets the use of the distribution network, deletes the unsatisfactory distributed power sources from the power source list, completes the primary deletion of the power source list, performs a retrospective analysis on the stability of the remaining distributed power sources, deletes the distributed power sources that fail the stability retrospective analysis from the power source list, completes the secondary deletion of the power source list, and dynamically adjusts the output power of each distributed power source according to the stability retrospective analysis results. The analysis system dynamically adjusts the output of the distributed power source according to the retrospective analysis results to match it with the load demand of the distribution network, and ensures the stability and safety of the distribution network when the load is large.

[0034] 2. The present invention periodically obtains the backtracking analysis results and usage status of the distributed power source to determine whether the distribution network still needs to use the distributed power source, and generates a management strategy based on the judgment result. After analyzing the distributed power source, the analysis system determines whether the distributed power source still needs to be used, thereby improving the stability and scheduling flexibility of the subsequent operation of the distribution network, reducing unnecessary power activation, and optimizing the operation cost of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1: Please refer to Figure 1 As shown, the comprehensive load characteristic analysis method of the distribution network containing distributed power sources described in this embodiment includes the following steps:

[0039] The analysis system obtains the information of all distributed power sources connected to the distribution network through the distribution network management system, and initially marks each distributed power source, builds a power list for all marked distributed power sources, and regularly performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the storage power of the distributed power source meets the use of the distribution network, deletes the unsatisfactory distributed power sources from the power list, completes a primary deletion of the power list, performs a retrospective analysis on the stability of the remaining distributed power sources, deletes the distributed power sources that fail the stability retrospective analysis from the power list, completes a secondary deletion of the power list, and dynamically adjusts the output power of each distributed power source according to the stability retrospective analysis results. After regularly obtaining the retrospective analysis results and usage status of the distributed power source, it determines whether the distribution network still needs to use the distributed power source, and generates a management strategy based on the judgment result.

[0040] This application periodically performs stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, it first determines whether the stored power of the distributed power source meets the use of the distribution network, deletes the unsatisfactory distributed power sources from the power source list, completes the first deletion of the power source list, performs a retrospective analysis on the stability of the remaining distributed power sources, deletes the distributed power sources that fail the stability retrospective analysis from the power source list, completes the second deletion of the power source list, and dynamically adjusts the output power of each distributed power source according to the stability retrospective analysis results. The analysis system dynamically adjusts the output of the distributed power source according to the retrospective analysis results to match it with the load demand of the distribution network, and ensures the stability and safety of the distribution network when the load is large.

[0041] This application determines whether the distribution network still needs to use the distributed power source by regularly obtaining the backtracking analysis results and usage status of the distributed power source, and generates a management strategy based on the judgment result. After analyzing the distributed power source, the analysis system determines whether the distributed power source still needs to be used, thereby improving the stability and scheduling flexibility of the subsequent operation of the distribution network, reducing unnecessary power activation, and optimizing the operation cost of the power grid.

[0042] Embodiment 2: The analysis system obtains information of all distributed power sources connected to the distribution network through the distribution network management system, and initially marks each distributed power source, and constructs a power source list for all marked distributed power sources, including the following steps:

[0043] The distribution network management system collects all the information of the connected distributed power sources, including but not limited to the following:

[0044] Distributed power type (photovoltaic, wind power, energy storage, etc.). Access point location and access node number. Rated power, current output power and energy storage status. Environmental condition related parameters (such as light intensity, wind speed, etc.). Obtain distribution network topology information and clarify the connection relationship between distributed power sources and the power grid. Verify the validity of the collected data and remove duplicate, missing or erroneous information. Format the power information uniformly and initialize it into a data structure that can be recognized by the analysis system.

[0045] According to the operating characteristics and access conditions of distributed power sources, set the initial marking rules, including: Classify and mark according to the type of distributed power sources (photovoltaic, wind power, energy storage, etc.). Mark the rated capacity and current output power level of each power source. Mark the specific location and node number of the access point of the power source in the distribution network topology. Generate a unique set of marking attributes for each distributed power source and store them as the initial attributes of the power source.

[0046] Summarize the information of all distributed power sources that have completed the initial tagging and build a power list containing all connected power sources. The list includes: power source ID, connection location, type, capacity, operating status, stability level, etc. Set up a dynamic update mechanism to regularly update the power list based on the real-time collected distributed power source operating status (such as output power, remaining energy storage capacity, etc.).

[0047] The power supply list is associated with the distribution network topology information to clarify the impact of each distributed power supply on the distribution network operation. Based on the power supply list, basic data support is provided for subsequent stability analysis, output power optimization and management strategy generation.

[0048] The constructed power supply list is stored in the database of the analysis system to ensure efficient call and update of the list in subsequent operations. A visual interface of the list is provided to facilitate grid operators to view and manage the status of connected distributed power sources.

[0049] Regularly perform stability analysis on each connected distributed power source, including the following steps:

[0050] After obtaining the operating data of the power generation equipment of each distributed power source, the equipment operating data is substituted into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of each distributed power source, and the expression is: In the formula, abnormal is the abnormal index, is the operating data of each power generation equipment of the distributed power source, n represents the number of operating data of the power generation equipment, {G1, G2, ..., G n} is the regression coefficient, and the regression coefficient is greater than 0;

[0051] The larger the abnormal index is, the worse the stability of the distributed power source is. The obtained abnormal index is compared with the preset index threshold. The index threshold is used to judge whether the stability of the distributed power source is good or bad. If the abnormal index is greater than the index threshold, the stability of the distributed power source is judged to be poor. If the abnormal index is less than or equal to the index threshold, the stability of the distributed power source is judged to be good.

[0052] The logical factors of the abnormal index when used in the present invention are as follows: taking the influence of the operating data of each power generation equipment on the operating stability of the distributed power supply as an example, the first is the indicator, that is, the factor that causes the change in the operating stability of the distributed power supply (the present invention refers to the influence of the operating data of each power generation equipment on the operating stability of the distributed power supply); the second is the weight of these indicators, that is, the proportion of each major influencing data when it is generated; the third is the operation equation, that is, what kind of mathematical operation process is used to obtain the result, and the abnormal index is obtained by calculating the indicators with their respective weights through the operation equation.

[0053] The main influencing data obtained from the sample were transformed and processed into a data language recognized by computer software; secondly, these evaluation factors were analyzed by Logistic regression using SPSS software to screen out factors and their weights that are significantly correlated with the results; thirdly, the evaluation factors and weights were substituted into the Logistic regression equation for calculation to obtain the results, which are as follows:

[0054] First, ensure the integrity of the main effect data, handle missing values ​​and outliers, and convert the data into a format that SPSS software can recognize. Usually, the data is stored in .csv, .xlsx and other formats, and then imported into SPSS. Open SPSS software, import the processed data file, and transform the variables as needed. For example, for continuous variables, standardize or normalize them. Select the "Analyze" menu, then select the "Binary Logistic" option under "Regression". In the dialog box, add the dependent variable (outcome) and the independent variable (main effect data) to the corresponding boxes. SPSS will fit the Logistic regression based on the selected variables. Return to the model. In the output results, you will see the coefficients, standard errors, p-values ​​and other information of the model. Check the coefficients and p-values ​​in the output results to determine which variables are significantly correlated with the results. Usually, a p-value less than 0.05 is considered significant. While fitting the model, use variable selection methods, such as stepwise regression, to help screen the most relevant factors. According to the coefficients of the Logistic regression model, the size of the coefficient reflects the degree of influence of each factor on the result, and the positive and negative signs of the coefficients indicate the direction of the influence. After obtaining the significant factors and their coefficients, the Logistic regression equation is obtained, which is used to calculate the probability of each sample and then predict the results.

[0055] In order to better illustrate the above solution, this application provides the following examples;

[0056] If the distributed power source is a wind power station, the equipment operation data obtained include the vibration amplitude of the rotor bearing of the wind turbine, the decibel of gearbox noise, and wind speed fluctuation;

[0057] Install vibration sensors or acceleration sensors on bearings or nearby mechanical structures to collect the vibration amplitude of rotor bearings in real time, and install noise sensors (microphones) or acoustic sensors outside the gearbox or near key components to collect gearbox noise decibels in real time;

[0058] Among them, the calculation logic of wind speed fluctuation is: obtain the wind speed at multiple time points in the environment where the wind turbine is located within the monitoring period, calculate the wind speed standard deviation based on the wind speed at multiple time points, and the wind speed standard deviation is the wind speed fluctuation. The wind speed standard deviation is calculated through the existing known standard deviation.

[0059] The rotor bearing vibration amplitude, gearbox noise decibel and wind speed wave are substituted into the linear regression analysis algorithm for comprehensive calculation to obtain the abnormal index of the wind power station. The expression is: In the formula, abnormal is the abnormal index, are the vibration amplitude of the rotor bearing, the decibel of gearbox noise and the wind speed fluctuation respectively. {G1, G2, G3} are the regression coefficients, and the regression coefficients are greater than 0.

[0060] Rotor bearings are key mechanical components of wind turbines, and the vibration amplitude reflects the operating status of the bearings and the health of the mechanical structure.

[0061] The vibration amplitude is small (within the normal range): It indicates that the bearing runs smoothly, there is no abnormality in the mechanical structure, and the operation stability is high.

[0062] Increased vibration amplitude (beyond the normal range): Increased vibration may be caused by bearing wear, imbalance, poor lubrication or mechanical looseness, indicating that the fan is operating abnormally and its stability is reduced.

[0063] Excessive vibration amplitude (serious abnormality): Excessive vibration can cause fatigue damage, increase the risk of mechanical component failure, and may cause wind turbine shutdown or equipment damage.

[0064] Gearbox noise is an important indicator of the internal operating status of the gearbox, and the noise level is usually closely related to the gear meshing quality, wear degree and lubrication status.

[0065] The noise decibel is low (within the normal range): it indicates that the gear runs smoothly, is in good lubrication condition, and has high operating stability.

[0066] Increased noise decibels (beyond the normal range): Increased noise may be caused by gear wear, poor meshing or insufficient lubrication, indicating that there is an abnormality inside the gearbox and reduced operating stability.

[0067] Noise decibels are too high (serious abnormality): Excessive noise may be accompanied by increased vibration or damage to internal components, which will significantly reduce operating stability and require immediate shutdown for maintenance.

[0068] Wind speed fluctuations directly affect the operating load and output power of wind turbines. Excessive fluctuations may lead to unstable operation or even protective shutdown.

[0069] Wind speed fluctuations are small (within normal range): wind speed changes steadily, the fan operating state is stable, power output is stable, and operating stability is high.

[0070] Increased wind speed fluctuations (beyond the normal range): Increased wind speed fluctuations will cause the wind turbine to frequently adjust the blade angle and power generation, increase the mechanical load and control system pressure, and reduce operating stability.

[0071] Excessive wind speed fluctuations (violent fluctuations): Violent fluctuations may trigger the wind turbine’s automatic protection system, causing shutdown or even fatigue damage to mechanical components.

[0072] If the distributed power source is a photovoltaic power station, the equipment operation data obtained include the photovoltaic panel temperature rise rate, the power factor deviation amplitude of the photovoltaic inverter, the partial discharge intensity of the step-up transformer, and the light intensity fluctuation;

[0073] The temperature of the photovoltaic panel is monitored by a temperature sensor installed on the back of the photovoltaic panel. The temperature difference is obtained by subtracting the temperature at the previous moment from the current moment. The temperature difference is divided by the monitoring time to obtain the temperature rise rate of the photovoltaic panel;

[0074] Subtract the actual power factor of the photovoltaic inverter from the standard power factor of the photovoltaic inverter to obtain the deviation amplitude of the power factor of the photovoltaic inverter;

[0075] The method of obtaining light intensity fluctuation is similar to that of wind speed fluctuation, and will not be further described in this application.

[0076] The PD intensity of the step-up transformer is obtained online through the existing PD monitor, which can detect the PD activity in the transformer in real time;

[0077] Partial discharge monitors include:

[0078] OMICRON (such as: OMICRON-PD-Testing-System);

[0079] Megger (such as: Megger-PD-Detection-Equipment);

[0080] HIOKI (such as: HIOKI-PD-Monitoring-Instruments).

[0081] These instruments usually provide a real-time numerical display, enabling precise monitoring of the intensity of partial discharges.

[0082] Substitute the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation amplitude, the step-up transformer partial discharge intensity and the light intensity fluctuation into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of the photovoltaic power station. The expression is: In the formula, abnormal is the abnormal index, They are the temperature rise rate of the photovoltaic panel, the power factor deviation amplitude, the partial discharge intensity of the step-up transformer and the light intensity fluctuation. {G1, G2, G3, G4} are the regression coefficients, and the regression coefficients are greater than 0.

[0083] A high rate of temperature rise indicates that the PV panels may be abnormal due to poor heat dissipation, rapid changes in ambient temperature, or system failure.

[0084] High temperatures may reduce the output efficiency of photovoltaic modules, increase the risk of hot spot effects, and even cause module damage.

[0085] The temperature rise rate is small or normal: the system runs relatively stably, the heat dissipation capacity of the components matches, and the environmental conditions are suitable.

[0086] The greater the rate of temperature rise, the worse the operational stability of the photovoltaic panel. Long-term high temperature may have an adverse effect on the life of the component.

[0087] The power factor has a large deviation: It indicates that the ratio of reactive power to active power is abnormal, which may be caused by inverter regulation failure, grid voltage fluctuation or unstable PV module output. A large deviation will increase the reactive power compensation pressure of the grid and affect the power quality.

[0088] The power factor deviation is small or close to the rated value (such as 0.95 or above): It indicates that the reactive power of the system and the grid is well matched and the operating status is stable.

[0089] The greater the power factor deviation, the worse the adaptability of the photovoltaic power station to the power grid, which may cause grid-connected operation failures or reduced efficiency.

[0090] High partial discharge intensity: indicates that the insulation condition of the transformer has deteriorated, such as insulation aging, air gap discharge or other dielectric defects, which may develop into insulation breakdown. Continuous high-intensity discharge will shorten the life of the transformer and increase the risk of failure.

[0091] The local discharge intensity is low or there is no discharge: it indicates that the insulation performance of the transformer is good and the operating status is normal.

[0092] The higher the partial discharge intensity, the worse the operating stability of the step-up transformer, which may cause system shutdown or power outage in severe cases.

[0093] Large fluctuations in light intensity: This may be caused by rapid changes in cloud cover, sudden weather changes, or partial shading, which can cause frequent fluctuations in the output power of photovoltaic modules. Excessive fluctuations in the output power of photovoltaic power stations can pose challenges to the stability of inverters and grid connections.

[0094] Light intensity fluctuations are small or stable: The photovoltaic system has a stable output power and high operating stability.

[0095] The greater the fluctuation of light intensity, the worse the stability of the photovoltaic power station's output power, which may affect the grid connection quality and power generation efficiency.

[0096] When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power source meets the use of the distribution network, delete the distributed power source that does not meet the requirements from the power source list, and complete the deletion process of the power source list, including the following steps:

[0097] The analysis system monitors the real-time load of the distribution network. When the real-time load is greater than the load threshold, it indicates that the load of the distribution network has increased, and it is necessary to supplement the current through distributed power sources to reduce the operating burden of the substation;

[0098] If the stored power of the distributed power source is less than the power threshold, it is determined that the stored power of the distributed power source does not meet the needs of the distribution network, and the unsatisfactory distributed power source is deleted from the power source list, completing a deletion process of the power source list.

[0099] Perform a retrospective analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability retrospective analysis from the power source list, and complete the secondary deletion process of the power source list, including the following steps:

[0100] The stability of the distributed power in the power list after a deletion is retrospectively analyzed. The logic of the retrospective analysis is: obtain the abnormal index obtained at multiple time points in the history of the distributed power, and calculate the mean and standard deviation of the abnormal index based on the abnormal index obtained at multiple time points. The expression is: In the formula, abnormal avg

[0101] is the mean of the abnormal index, abnormal Q is the standard deviation of the abnormal index, m is the number of time points, abnormal i is the abnormal index value obtained at the i-th time point;

[0102] The obtained abnormal index mean is divided by the abnormal index standard deviation to obtain the overall abnormal coefficient of the distributed power source, and the retrospective analysis of the distributed power source is completed;

[0103] The present application conducts a comprehensive retrospective analysis on the abnormal indexes obtained from the history of distributed power sources for multiple times. The larger the value of the overall abnormality coefficient obtained, the worse the overall stability of the distributed power source over a period of time. The obtained overall abnormality coefficient is compared with a preset second coefficient threshold. If the overall abnormality coefficient of the distributed power source is greater than the second coefficient threshold, the distributed power source stability retrospective analysis is judged to be unqualified, and the distributed power source is deleted from the power source list for a second time, and the distributed power sources with an overall abnormality coefficient less than or equal to the second coefficient threshold are retained.

[0104] Dynamically adjust the output power of each distributed power source according to the stability backtracking analysis results, including the following steps:

[0105] In the power source list that has completed the secondary deletion process, the larger the value of the overall abnormal coefficient of the distributed power source is, the worse the stability of the distributed power source is;

[0106] Therefore, the output power of each distributed power source is dynamically adjusted by obtaining the overall abnormal coefficient. The adjustment algorithm is: new =dl old / ln(yc z +1), where dl new is the output power after adjustment, dl old is the initial preset output power (usually obtained based on the historical output power of the distributed power source), yc z is the overall abnormal coefficient;

[0107] That is, in the power supply list, the larger the overall abnormal coefficient of the distributed power supply, the more it is necessary to reduce the output power of the distributed power supply, which not only ensures the stable operation of the distributed power supply, but also ensures the stable operation of the distribution power supply.

[0108] It should be noted that when the output power of some distributed power sources is reduced, the total output power of all distributed power sources supplying power to the distribution network will be reduced. At this time, if there are idle distributed power sources in the power supply list, the output power will be compensated by the idle distributed power sources. If there are no idle distributed power sources, the output power will be compensated by the distributed power sources whose overall abnormality coefficient is less than or equal to the first coefficient threshold. The first coefficient threshold is less than the second coefficient threshold. If the overall abnormality coefficient is less than or equal to the first coefficient threshold, it indicates that the stability of the distributed power source is excellent and the output power of the distributed power source can be increased.

[0109] After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result, including the following steps:

[0110] Regularly obtain the overall abnormal coefficient of distributed power sources, the usage frequency of distributed power sources, and the accumulated idle time;

[0111] Obtain the historical usage times of the distributed power source, and obtain the usage frequency by dividing the historical usage times by the monitoring time. The greater the usage frequency, the higher the confidence of the distributed power source in the distribution network, and the more it needs to be used;

[0112] The idle cumulative time is obtained through the management system log of the distributed power source. The idle time of the distributed power source in the past when it was not in use is obtained. The idle cumulative time is summed up for multiple times when it was not in use to obtain the idle cumulative time. The longer the idle cumulative time is, the lower the confidence of the distributed power source in the distribution network is, and the less it needs to be used;

[0113] The overall abnormal coefficient, usage frequency and accumulated idle time are calculated comprehensively to obtain the management factor of distributed power supply, and the expression is: In the formula, GL z is the management factor, δ is the usage frequency, yc z is the overall abnormality coefficient, ε is the accumulated idle time, α, β, and γ are the adjustment coefficients of the usage frequency, the overall abnormality coefficient, and the accumulated idle time, and α, β, and γ are all greater than 0;

[0114] The larger the management factor, the less the distributed power source needs to be used. The obtained management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is less than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is greater than the management threshold, it is determined that the distribution network does not need to retain the distributed power source.

[0115] The management strategy generated based on the judgment result is: the distributed power sources that need to be retained are classified into the retention set, and the distributed power sources that do not need to be retained are classified into the deletion set, and the retention set and the deletion set are sent to the distribution network management platform.

[0116] Embodiment 3: The comprehensive load characteristic analysis system of the distribution network containing distributed power sources described in this embodiment includes a power list construction module, a distributed power analysis module, and a management module;

[0117] Power list building module: obtains all distributed power information connected to the distribution network through the distribution network management system, and initially marks each distributed power source, builds a power list of all marked distributed power sources, and sends the power list to the distributed power analysis module and management module;

[0118] Distributed power supply analysis module: regularly conducts stability analysis on each connected distributed power supply. When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power supply meets the use of the distribution network, delete the distributed power supply that does not meet the requirements from the power supply list, complete the first deletion of the power supply list, perform a retrospective analysis on the stability of the remaining distributed power supplies, delete the distributed power supply that fails the stability retrospective analysis from the power supply list, complete the second deletion of the power supply list, and dynamically adjust the output power of each distributed power supply according to the stability retrospective analysis results. The stability retrospective analysis results are sent to the management module;

[0119] Management module: After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it determines whether the distribution network still needs to use the distributed power source, and generates a management strategy based on the judgment results.

[0120] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0121] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0122] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources, characterized in that: The analytical method comprises the following steps: The analysis system obtains information about all distributed power sources connected to the distribution network through the distribution network management system, initially marks each distributed power source, and constructs a power source list for all marked distributed power sources; Perform stability analysis on each connected distributed power source regularly. When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power source meets the use of the distribution network, delete the distributed power source that does not meet the requirements from the power source list, and complete the deletion of the power source list. Perform a back-analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability back-analysis from the power source list, complete the secondary deletion of the power source list, and dynamically adjust the output power of each distributed power source according to the stability back-analysis results; After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result.

2. The method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources according to claim 1, characterized in that: Regularly perform stability analysis on each connected distributed power source, including the following steps: After obtaining the operating data of the power generation equipment of each distributed power source, the equipment operating data is substituted into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of each distributed power source, and the expression is: In the formula, abnormal is the abnormal index, is the operating data of each power generation equipment of the distributed power source, {G1, G2, ..., G n } is the regression coefficient, and the regression coefficient is greater than 0; The obtained abnormal index is compared with the preset index threshold. The index threshold is used to judge whether the stability of the distributed power supply is good or poor. If the abnormal index is greater than the index threshold, the stability of the distributed power supply is judged to be poor. If the abnormal index is less than or equal to the index threshold, the stability of the distributed power supply is judged to be good.

3. The method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources according to claim 2, characterized in that: Perform a retrospective analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability retrospective analysis from the power source list, and complete the secondary deletion process of the power source list, including the following steps: A retrospective analysis is performed on the stability of the distributed power sources in the power source list after a deletion. The logic of the retrospective analysis is: obtain the abnormal index obtained at multiple time points in the history of the distributed power source, calculate the mean of the abnormal index and the standard deviation of the abnormal index based on the abnormal index obtained at multiple time points, and obtain the overall abnormal coefficient of the distributed power source by dividing the obtained mean of the abnormal index by the standard deviation of the abnormal index; The obtained overall abnormality coefficient is compared with the preset second coefficient threshold. If the overall abnormality coefficient of the distributed power source is greater than the second coefficient threshold, the distributed power source stability retrospective analysis is judged to be unqualified and the distributed power source is deleted from the power source list for the second time.

4. The method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources according to claim 3 is characterized in that: The anomaly index mean and the anomaly index standard deviation are calculated based on the anomaly index obtained at multiple time points. The expression is: In the formula, abnormal avg is the mean of the abnormal index, abnormal Q is the standard deviation of the abnormal index, m is the number of time points, abnormal i The abnormality index value obtained at the i-th time point.

5. The method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources according to claim 4, characterized in that: Dynamically adjust the output power of each distributed power source according to the stability backtracking analysis results, including the following steps: The output power of each distributed power source is dynamically adjusted by obtaining the overall abnormal coefficient. The adjustment algorithm is: new =dl old / ln(yc z +1), where dl new is the output power after adjustment, dl old is the initial preset output power, yc z is the overall abnormal coefficient.

6. The method for analyzing the comprehensive load characteristics of a distribution network containing distributed power sources according to claim 5, characterized in that: After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it is determined whether the distribution network still needs to use the distributed power source, and a management strategy is generated based on the determination result, including the following steps: Regularly obtain the overall abnormal coefficient of distributed power sources, the usage frequency of distributed power sources, and the accumulated idle time; The overall abnormal coefficient, usage frequency and accumulated idle time are calculated comprehensively to obtain the management factor of distributed power supply, and the expression is: In the formula, GL z is the management factor, δ is the usage frequency, yc z is the overall abnormality coefficient, ε is the accumulated idle time, α, β, and γ are the adjustment coefficients of the usage frequency, the overall abnormality coefficient, and the accumulated idle time, and α, β, and γ are all greater than 0; The obtained management factor is compared with the preset management threshold. The management threshold is used to determine whether the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is less than or equal to the management threshold, it is determined that the distribution network still needs to retain the distributed power source. If the management factor of the distributed power source is greater than the management threshold, it is determined that the distribution network does not need to retain the distributed power source. The management strategy generated based on the judgment result is: the distributed power sources that need to be retained are classified into the retention set, and the distributed power sources that do not need to be retained are classified into the deletion set, and the retention set and the deletion set are sent to the distribution network management platform.

7. The method for analyzing comprehensive load characteristics of a distribution network containing distributed power sources according to claim 2, characterized in that: If the distributed power source is a photovoltaic power station, the power generation equipment operation data of the photovoltaic power station is obtained, including the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation amplitude, the boost transformer partial discharge intensity and the light intensity fluctuation; Substitute the photovoltaic panel temperature rise rate, the photovoltaic inverter power factor deviation amplitude, the step-up transformer partial discharge intensity and the light intensity fluctuation into the linear regression analysis algorithm for comprehensive calculation, so as to obtain the abnormal index of the photovoltaic power station. The expression is: In the formula, abnormal is the abnormal index, They are the temperature rise rate of the photovoltaic panel, the power factor deviation amplitude, the partial discharge intensity of the step-up transformer and the light intensity fluctuation. {G1, G2, G3, G4} are the regression coefficients, and the regression coefficients are greater than 0.

8. A comprehensive load characteristic analysis system for a distribution network containing distributed power sources, used to implement the analysis method according to any one of claims 1 to 7, characterized in that: Includes power list building module, distributed power analysis module, and management module; Power list building module: obtains all distributed power information connected to the distribution network through the distribution network management system, initially marks each distributed power source, and builds a power list for all marked distributed power sources; Distributed power analysis module: regularly conducts stability analysis on each connected distributed power source. When the load of the distribution network exceeds the preset load threshold, first determine whether the storage power of the distributed power source meets the use of the distribution network, delete the distributed power sources that do not meet the requirements from the power source list, complete the first deletion of the power source list, perform a retrospective analysis on the stability of the remaining distributed power sources, delete the distributed power sources that fail the stability retrospective analysis from the power source list, complete the second deletion of the power source list, and dynamically adjust the output power of each distributed power source according to the stability retrospective analysis results; Management module: After regularly obtaining the backtracking analysis results and usage status of the distributed power source, it determines whether the distribution network still needs to use the distributed power source, and generates a management strategy based on the judgment results.

Images