Power terminal device energy efficiency optimization method and device for smart grid

Abstract

The present invention discloses a method and device for optimizing the energy efficiency of power terminal equipment for smart grids, and relates to the field of power technology. The method comprises: obtaining a power terminal distribution network including multiple power terminals and their layout positions in a target power grid. Extracting the operating data of multiple power terminals within a preset monitoring window to form multiple monitoring operation data sets, and performing centralized value analysis on them to generate multiple explicit operation centralized value sets. Extracting the long-time series operation data of multiple power terminals, performing segmented iterative analysis, and determining multiple implicit operation factors. Based on the power terminal distribution network and its layout position, sampling of neighboring terminals is performed, and correlation analysis is performed in combination with the sampling results and implicit operation factors to obtain multiple energy efficiency optimization background factors. Energy efficiency optimization is implemented on multiple power terminals using energy efficiency optimization background factors and explicit operation centralized value sets, thereby achieving the technical effect of improving equipment coordination and adaptability to working conditions and improving energy efficiency performance.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and particularly to a method and device for optimizing the energy efficiency of power terminal devices for a smart grid. Background Art

[0002] In smart grid technology, the intelligent management of power terminal devices is the key to improving the operation efficiency of the power grid. In the prior art, when optimizing the energy efficiency of power terminal devices, the operation conditions of each terminal device at present and / or within a short period of time are often analyzed based on the static characteristics and single working conditions of the devices, and then energy efficiency optimization is carried out according to the analysis results. There are technical problems such as low device collaboration level, poor working condition adaptability and energy efficiency performance. Summary of the Invention

[0003] The present invention provides a method and device for optimizing the energy efficiency of power terminal devices for a smart grid, so as to solve the technical problems of low device collaboration level, poor working condition adaptability and energy efficiency performance in the prior art, and achieve the technical effects of improving device collaboration and working condition adaptability and improving energy efficiency performance.

[0004] In a first aspect, the present invention provides a method for optimizing the energy efficiency of power terminal devices for a smart grid, wherein the method includes:

[0005] Obtain the power terminal distribution network of the target power grid, wherein the power terminal distribution network includes K power terminals and K terminal layout positions, and K is an integer greater than or equal to 1.

[0006] Based on the sensor integration array arranged on each power terminal, extract the operation data of the K power terminals in a preset monitoring window to obtain K monitoring operation data sets, wherein each monitoring operation data set includes all operation data of an operation index within the preset monitoring window.

[0007] Perform centralized value analysis on the K monitoring operation data sets to obtain K explicit operation centralized value sets.

[0008] Traverse the K power terminals to extract long-time series operation data to obtain K long-time series operation data sets.

[0009] Based on the K long-time series operation data sets, perform data segmentation iterative analysis to determine K implicit operation factors.

[0010] Perform neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and perform correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors.

[0011] Perform energy efficiency optimization on the K power terminals according to the K energy efficiency optimization background factors and the K explicit operation concentration value sets, and obtain K energy efficiency optimization solutions.

[0012] In a second aspect, the present invention also provides an energy efficiency optimization device for power terminal equipment in a smart grid. The device includes:

[0013] A distribution network acquisition module, which is used to acquire the power terminal distribution network of the target power grid. The power terminal distribution network includes K power terminals and K terminal layout positions, where K is an integer greater than or equal to 1.

[0014] An operation data extraction module, which is used to extract the operation data of the K power terminals in a preset monitoring window based on the sensor integration array arranged on each power terminal, and obtain K monitoring operation data sets. Each monitoring operation data set includes all the operation data of an operation index within the preset monitoring window.

[0015] A concentration value analysis module, which is used to perform concentration value analysis on the K monitoring operation data sets to obtain K explicit operation concentration value sets.

[0016] A long-time series extraction module, which is used to traverse the K power terminals to extract long-time series operation data and obtain K long-time series operation data sets.

[0017] A segmented iterative analysis module, which is used to perform data segmented iterative analysis based on the K long-time series operation data sets to determine K implicit operation factors.

[0018] A background factor positioning module, which is used to perform near-neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and perform correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors.

[0019] An optimization decision module, which is used to perform energy efficiency optimization on the K power terminals according to the K energy efficiency optimization background factors and the K explicit operation concentration value sets, and obtain K energy efficiency optimization solutions.

[0020] The present invention discloses a method and device for optimizing the energy efficiency of power terminal equipment for smart grids, including: obtaining the distribution network information of power terminals in a target grid, where the power terminal distribution network consists of K power terminals and their corresponding installation positions, and K is an integer not less than 1; based on the sensor integration array installed on each power terminal, extracting the operation data of each power terminal within a specified monitoring window to obtain K monitoring operation data sets, where each set contains all operation index data within the monitoring window; performing centralized value analysis on the K monitoring operation data sets to generate K explicit operation centralized value sets; traversing the K power terminals one by one, extracting their long-time series operation data to obtain K long-time series operation data sets; using the K long-time series operation data sets to carry out data segmentation and iterative analysis to identify K implicit operation factors; combining the distribution network of power terminals and their installation positions, implementing near-neighbor terminal sampling, and performing correlation analysis based on the sampling results and the K implicit operation factors to determine K background factors for energy efficiency optimization; finally, based on the K background factors for energy efficiency optimization and the K explicit operation centralized value sets, carrying out energy efficiency optimization for the K power terminals and outputting K corresponding energy efficiency optimization schemes. The method and device for optimizing the energy efficiency of power terminal equipment for smart grids disclosed by the present invention solve the technical problems of low equipment collaboration level, poor working condition adaptability and energy efficiency performance, and achieve the technical effects of improving equipment collaboration and working condition adaptability and improving energy efficiency performance. Brief Description of the Drawings

[0021] Figure 1 It is a schematic flow chart of the method for optimizing the energy efficiency of power terminal equipment for smart grids of the present invention.

[0022] Figure 2 It is a schematic structural diagram of the device for optimizing the energy efficiency of power terminal equipment for smart grids of the present invention.

[0023] Description of the reference numerals: Distribution network acquisition module 11, operation data extraction module 12, centralized value analysis module 13, long-time series extraction module 14, segmentation and iteration analysis module 15, background factor positioning module 16, optimization decision module 17. Detailed Description of the Embodiment

[0024] In the embodiments of the present invention, the overall idea adopted for solving the technical problems of low equipment collaboration level, poor working condition adaptability and energy efficiency performance existing in the prior art is as follows:

[0025] First, obtain the power terminal distribution network of the target power grid. The power terminal distribution network includes multiple power terminals and multiple terminal deployment locations, where the numbers are all integers greater than or equal to 1. Then, based on the sensor integration arrays deployed on each power terminal, extract the operation data of the multiple power terminals within a preset monitoring window to obtain multiple monitoring operation data sets. Each monitoring operation data set contains all the operation data of an operation index within the preset monitoring window. Next, perform a central value analysis on the multiple monitoring operation data sets to obtain multiple explicit operation central value sets. Then, traverse the multiple power terminals to extract long-term operation data to obtain multiple long-term operation data sets. Furthermore, based on the multiple long-term operation data sets, perform data segmentation iterative analysis to determine multiple implicit operation factors. Subsequently, based on the power terminal distribution network and the multiple terminal deployment locations, perform neighbor terminal sampling on the multiple power terminals, and perform correlation analysis based on the sampling results and the multiple implicit operation factors to determine multiple energy efficiency optimization background factors. Finally, based on the multiple energy efficiency optimization background factors and the multiple explicit operation central value sets, perform energy efficiency optimization on the multiple power terminals to obtain multiple energy efficiency optimization solutions.

[0026] The following will combine the specification drawings and specific implementation manners to elaborate on the above technical solutions in detail to better understand the above technical solutions. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments only used to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. In addition, it should be noted that for the sake of description, only the parts related to the present invention are shown in the drawings rather than all.

[0027] Embodiment 1

[0028] Figure 1 It is a flow chart of the method for optimizing the energy efficiency of power terminal equipment for the smart grid of the present invention. The method includes:

[0029] Obtain the power terminal distribution network of the target power grid. The power terminal distribution network includes K power terminals and K terminal deployment locations, where K is an integer greater than or equal to 1.

[0030] Specifically, the power terminal distribution network is used to describe the specific distribution of power terminal equipment in the target power grid. Among them, the power terminal is the basic node in the target power grid, including various power equipment (such as transformers, circuit breakers, power metering devices, smart meters, load nodes, etc.). The terminal deployment location represents the specific location of each power terminal, including the physical location, geographical coordinates of each power terminal, and the connection relationship in the power grid topology.

[0031] Optionally, the deployment location of the terminal is also associated with information such as the altitude of the terminal device and the type of installation location (such as indoor or outdoor).

[0032] Exemplarily, to obtain the power terminal distribution network of the target power grid, first, obtain the unique identifier of each power terminal (such as terminal ID, device number, etc.), and this terminal identification data can come from the device management system of the power grid (such as SCADA system, EMS system) or the label information of on-site devices. Then, through on-site survey, GIS system or power grid management system (such as DMS system), collect the deployment location of each power terminal, including its geographical coordinates (such as GPS coordinates) and topological location (such as node number in the power grid topology). Next, through the power grid topology diagram or the network topology management system of the power dispatching center, determine the connection relationship between each power terminal.

[0033] Optionally, the power terminal distribution network of the target power grid is represented by a graph structure. First, take each power terminal as a node to form a node set, and the attributes of each node include: unique identifier, deployment location, device type, device status, etc. Then, according to the power connection attributes (such as voltage level, transmission power, fault status, etc.), define the connection relationship between nodes to form an edge set. Finally, combine the node set and the edge set to construct the power terminal distribution network of the target power grid. The power terminal distribution network constructed through the above steps realizes the graphical display of the target power grid and effectively describes the distribution of power terminals in the power grid.

[0034] Based on the sensor integration array deployed on each power terminal, extract the operation data of the K power terminals in the preset monitoring window to obtain K monitoring operation data sets, where each monitoring operation data set includes all the operation data of an operation index within the preset monitoring window.

[0035] Specifically, the K monitoring operation data sets correspond to K types of operation indexes, such as voltage, current, reactive power, harmonics, etc., and each monitoring operation data set contains at most the data groups corresponding to K power terminals. In other words, each monitoring operation data set contains all the operation data of the power terminals required for monitoring the corresponding operation index within the preset monitoring window.

[0036] Specifically, the preset monitoring window is the operation data monitoring and acquisition range defined based on the optimized computing power of the target power grid, including the acquisition time period of the operation data and the sampling frequency of the data. Optionally, the preset monitoring window is represented by the monitoring start time and end time, and the window size of the monitoring window is set according to the actual application scenario. For example, short-term monitoring windows (such as 1 second, 10 seconds, 1 minute, etc.) are used to identify short-term state fluctuations of power terminals (such as instantaneous overload, short-term power fluctuations).

[0037] The above steps are based on the sensor integration array to conduct multi-dimensional monitoring of the power terminal, which can form a detailed set of monitoring operation data, thus providing strong data support for the state analysis and evaluation of the power terminal.

[0038] In some embodiments, the sensor integration array includes a voltage sensor, a current sensor, a power sensor, a temperature sensor, and a vibration sensor.

[0039] Specifically, the voltage sensor is used to measure the real-time voltage value of the power terminal to evaluate whether the power terminal operates within the rated voltage range and identify voltage fluctuations, undervoltage, or overvoltage conditions. The current sensor is used to monitor the current output or input value of the power terminal, which can reflect the load status of the power terminal and help identify overload, short circuit, and abnormal current fluctuations. The power sensor is used to monitor the power output of the power terminal, including active power and reactive power, and further reflect the energy efficiency level, load capacity, and power factor change of the power equipment. The temperature sensor is used to detect the surface temperature or internal temperature of the power terminal equipment, which helps identify whether there are overheating, poor heat dissipation, or abnormal ambient temperature conditions in the power equipment and assist in the energy efficiency level evaluation. The vibration sensor monitors the possible vibrations or mechanical failures (such as loose transformer cores, abnormal vibrations of circuit breakers, etc.) during the operation of the power terminal equipment, thereby obtaining the mechanical structure health status of the power equipment.

[0040] Perform a central value analysis on the K sets of monitoring operation data to obtain K explicit operation central value sets.

[0041] Specifically, through the central value analysis of the K sets of monitoring operation data, identify the central value points of each power terminal in the monitoring data set, obtain the explicit operation central value sets of each set, and further determine the main operation status of each power terminal within a certain time window. This helps identify the overall trends and characteristics of the target power grid operation status and provides strong support for optimization and decision-making.

[0042] In some embodiments, performing a central value analysis on the K sets of monitoring operation data to obtain K explicit operation central value sets includes:

[0043] Extract the first set of monitored operation data from the K sets of monitored operation data, and calculate the mean of the first set of monitored operation data to obtain the first monitored operation mean. Using the first monitored operation mean as the search center, search in the first set of monitored operation data according to the preset concentration bandwidth to determine the first searched monitored operation data. Calculate the concentration densities of the first monitored operation mean and the first searched monitored operation data respectively. When the concentration density of the first searched monitored operation data is greater than or equal to the concentration density of the first monitored operation mean, obtain the first search direction. Update the first searched monitored operation data as the search center, and based on the first search direction, search in the first set of monitored operation data according to the preset concentration bandwidth until the difference in concentration densities between two adjacent searches is less than or equal to the preset concentration density difference, then stop the search. Take the search center corresponding to the maximum concentration density during the search process as the target search center, and take the monitored operation data corresponding to the target search center as the first explicit operation concentration value set. Perform concentration value analysis on the K sets of monitored operation data according to the preset concentration bandwidth to obtain the K explicit operation concentration value sets.

[0044] In some implementation manners, the first search direction is the direction of the first searched monitored operation data relative to the first monitored operation mean.

[0045] Specifically, first, from the monitored data sets of K power terminals, sequentially select the data set of each power terminal, and calculate the initial mean of the selected data set as the initial center point for search. Then, using the initial mean as the search center, perform a neighborhood search in the data set according to the preset concentration bandwidth. Among them, the concentration bandwidth is used to define the neighborhood range of data points during the search process (i.e., the search radius). In other words, the concentration bandwidth determines the size of the search area, and the search result is the set of data points within all concentration bandwidths, denoted as the first searched monitored operation data set. Next, calculate the concentration densities of the initial mean and the first searched monitored operation data set respectively. Among them, the concentration density represents the degree of data point concentration within the search area, and is calculated by the number of data points within the neighborhood, and is used to determine whether to continue the search for the concentration value. Then, judge the density change. If the concentration density of the first searched monitored operation data set is greater than or equal to the concentration density of the first monitored operation mean, determine the direction of the first searched monitored operation data relative to the first monitored operation mean as the first search direction, and set the mean of the first searched monitored operation data set as the new search center. Furthermore, repeat the neighborhood search process until the search center no longer changes significantly or the change amount of the concentration density is lower than the preset threshold (i.e., the search center tends to be stable), and take the search center corresponding to the maximum concentration density during the search process as the target search center, and regard the data point set corresponding to the target search center as the first explicit operation concentration value set.

[0046] Optionally, the kernel density estimation method is used to calculate the concentration density to smooth the noise data points and improve the robustness of the concentration value recognition. Among them, the kernel functions include Gaussian kernel, triangular kernel, Epanechnikov kernel, etc.

[0047] Optionally, during the search process, the concentration bandwidth is dynamically adjusted according to the distribution of the current search results to avoid excessive or insufficient search. For example, when the growth rate of the concentration density in the search direction is slow, the bandwidth can be appropriately increased to accelerate the convergence speed.

[0048] The above-mentioned concentration value analysis steps provide important data support for the state evaluation and risk identification of power terminals. Combining the kernel density estimation and the bandwidth adaptive adjustment strategy can improve the efficiency and accuracy of the analysis and ensure more stable and reliable analysis results.

[0049] Traverse the K power terminals to extract long-time series operation data, and obtain K long-time series operation data sets.

[0050] Specifically, the long-time series operation data of the K power terminals are obtained through a long-time monitoring window (such as 1 hour, 1 day, 1 week, etc.) to analyze the long-term operation trend and health status of the power terminals (such as the long-term increase in temperature and the cumulative change in mechanical vibration). Among them, the long-time series operation data has a relatively low acquisition frequency and a relatively long monitoring window to avoid the difficulty of processing and analysis caused by excessive data volume.

[0051] Based on the K long-time series operation data sets, perform data segmentation iterative analysis to determine K implicit operation factors.

[0052] In some embodiments, based on the K long-time series operation data sets, perform data segmentation iterative analysis to determine K implicit operation factors, including:

[0053] According to the time length of the preset monitoring window, segment the K long-time series operation data sets in the direction away from the preset monitoring window to obtain K long-time series segmented operation data clusters. Calculate the fluctuation variances of the K long-time series segmented operation data clusters respectively to obtain K segmented fluctuation variance clusters. Perform weighted calculation on the K segmented fluctuation variance clusters according to the preset weight allocation scheme to obtain the K implicit operation factors.

[0054] Specifically, the preset monitoring window is the short-term monitoring window when obtaining the above-mentioned K sets of monitoring operation data. The preset monitoring window is the time range for data segmentation and serves as the benchmark point for segmented iterative analysis. When segmenting the long-time series data set according to the time length of the preset monitoring window, the data is sequentially segmented in the direction away from the preset monitoring window to form multiple long-time series segmented data clusters. In other words, the segmentation is performed in the direction from the new to the old to form a set of K long-time series segmented data clusters, and each set of long-time series segmented data clusters is a data subset within a time period.

[0055] Specifically, the fluctuation variance is used to quantify the fluctuation characteristics of each segmented data cluster within the time period to identify the degree of data fluctuation and stability. Exemplarily, the fluctuation variances of each segmented data cluster in the K long-time series segmented operation data clusters are sequentially calculated to form a K-segment fluctuation variance cluster.

[0056] Specifically, the implicit operation factor refers to a comprehensive characteristic index extracted from the long-time series data through segmented fluctuation variance analysis and weight allocation, which is used to reflect the potential state or trend of the power terminal during long-term operation and represents the characteristic patterns that are not easily noticed in the data (such as long-term fluctuation trends, areas with frequent anomalies, load aging characteristics, etc.).

[0057] Specifically, the weighted calculation is performed on the segmented fluctuation variance clusters of each power terminal to obtain the implicit operation factor of each power terminal. Preferably, a lower weight is assigned to the segmented fluctuation variance of the time period far from the current preset monitoring window to reflect the importance of the recent state.

[0058] The above method steps can effectively identify the long-term fluctuation patterns of power terminals and reveal their hidden operating states through segmented iterative analysis of long-time series data, which helps subsequent optimization analysis and decision-making.

[0059] Based on the power terminal distribution network and the K terminal layout positions, near-neighbor terminal sampling is performed on the K power terminals, and correlation analysis is performed according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors.

[0060] Specifically, the operating status and energy efficiency level of each power terminal are affected not only by itself, but also by its topological position in the power grid, surrounding power terminals, and the overall characteristics of the system. Therefore, near-neighbor terminal sampling is performed on the power terminal distribution network and its deployment locations, and correlation analysis is combined with implicit operating factors to identify the background factors for energy efficiency optimization of power terminals, thereby providing important references for subsequent energy efficiency optimization and scheduling strategies. Among them, the background factors for energy efficiency optimization represent systematic factors that affect the energy efficiency level of power terminals (such as the index parameters of the power terminal itself or associated power terminals), which are jointly determined by the topological position, environmental factors, and the correlation characteristics of their implicit operating factors of the power terminal.

[0061] In some embodiments, based on the power terminal distribution network and the K terminal deployment locations, near-neighbor terminal sampling is performed on the K power terminals, and correlation analysis is performed according to the sampling results and the K implicit operating factors to determine K background factors for energy efficiency optimization, including:

[0062] Based on the power terminal distribution network and the K terminal deployment locations, first-order near-neighbor terminal sampling is performed on the K power terminals to obtain K first-order near-neighbor terminal sets. Based on the K first-order near-neighbor terminal sets and the K terminal deployment locations, second-order near-neighbor terminal sampling is performed in the power terminal distribution network to obtain K second-order near-neighbor terminal sets. Using the K first-order near-neighbor terminal sets and the K second-order near-neighbor terminal sets as indexes, the K implicit operating factors are matched to obtain K first-order near-neighbor implicit operating factor sets and K second-order near-neighbor implicit operating factor sets. According to the distance to the K power terminals, correlation weighted analysis is performed on the K first-order near-neighbor implicit operating factor sets, the K second-order near-neighbor implicit operating factor sets, and the K implicit operating factors to obtain the K background factors for energy efficiency optimization.

[0063] Specifically, first, to sample the near-neighbor nodes of each power terminal and identify the spatial correlation characteristics of each power terminal in the power grid to determine its environmental impact range in the power grid and its potential energy efficiency impact factors. Among them, near-neighbor nodes refer to other power terminal nodes in the power distribution network that have a short path distance from the target power terminal. Near-neighbor nodes can be determined according to physical distance (such as geographical coordinates) or topological distance (such as the shortest path between nodes). Exemplarily, when performing near-neighbor sampling, the influence range of sampling is defined according to a specific sampling strategy (such as setting a distance threshold or a hierarchical threshold).

[0064] Optionally, in the power terminal distribution network, near-neighbor sampling is performed on each power terminal node. First, each power terminal is traversed to set the current sampling center. Then, the adjacent node search algorithm (such as breadth-first search, depth-first search) is used to traverse the first-order neighbor nodes in the network to form K first-order neighbor terminal sets. Then, based on the determined K first-order neighbor terminal sets, with each first-order neighbor terminal as the sampling center, multiple first-order neighbor nodes of each first-order neighbor terminal are obtained and output as K second-order neighbor terminal sets.

[0065] Optionally, for each first-order neighbor node and the first-order neighbor nodes of each first-order neighbor node, its attribute data (such as operating status, location attribute, implicit operating factor, etc.) is recorded.

[0066] Furthermore, after completing the near-neighbor terminal sampling, correlation analysis is performed based on the operating status of the near-neighbor nodes and their implicit factors to identify the background factors affecting the energy efficiency of the power terminals. Exemplarily, correlation analysis is performed on the implicit operating factor of each power terminal and the implicit factors of its near-neighbor nodes to obtain the correlation in the spatial distribution of the implicit factors and form a correlation matrix of the implicit factors. The correlation matrix of the implicit factors characterizes the correlation strength of each power terminal in the dimension of energy efficiency impact, that is, it describes the mutual energy efficiency impact relationship between power terminals. Among them, the correlation analysis methods include Pearson correlation coefficient analysis, grey correlation degree analysis, etc.

[0067] Furthermore, according to the correlation matrix of the implicit factors and the operating characteristics of its near-neighbor terminals, an energy efficiency optimization background factor for each power terminal is constructed. This energy efficiency optimization background factor is a quantitative model of various factors affecting the energy efficiency of the power terminal. In other words, the K energy efficiency optimization background factors define how to quantitatively calculate the energy efficiency levels of the K power terminals to accurately identify the energy efficiency levels of each power terminal and provide data support for subsequent optimization strategies.

[0068] In some implementation manners, the first-order neighbor terminal is a terminal that is on the same line as the power terminal and adjacent in location in the power terminal distribution network. The second-order neighbor terminal is a terminal that is on the same line as the first-order neighbor terminal and adjacent in location in the power terminal distribution network.

[0069] Specifically, the first-order neighbor terminal refers to a power terminal that is on the same line as the target power terminal and adjacent in location in the power terminal distribution network. These terminals have the most direct power connection relationship with the target terminal and are usually in adjacent geographical locations or on the same physical line, that is, the power transmission path is the shortest, and they may directly share the same power grid branch. Therefore, their operating status and fluctuation characteristics have a greater impact on the energy efficiency of the target terminal.

[0070] Specifically, the second-order neighboring terminal refers to a power terminal that is on the same line and adjacent in location to the first-order neighboring terminal in the power terminal distribution network. In other words, the second-order neighboring terminal is usually in an indirectly connected state with the target terminal, but is still affected by the same line or branch. The energy efficiency impact of the second-order neighboring terminal on the target terminal is slightly weaker than that of the first-order neighboring terminal. However, due to being on the same line, the abnormal state of the second-order neighboring terminal may still be transmitted to the target terminal through line fluctuations or load changes.

[0071] The above method of obtaining neighboring terminals based on different levels can more accurately quantify the factors affecting the energy efficiency of the target terminal, thereby providing more accurate input data for the construction of the energy efficiency optimization background factor and improving the model accuracy of the energy efficiency optimization background factor.

[0072] Perform energy efficiency optimization for the K power terminals according to the K energy efficiency optimization background factors and the K explicit operating concentration value sets to obtain K energy efficiency optimization solutions.

[0073] Optionally, define the objective function of energy efficiency optimization with the K energy efficiency optimization background factors, determine the parameter selection space of energy efficiency optimization according to the K explicit operating concentration value sets, and combine optimization algorithms (such as genetic algorithm, particle swarm optimization, echo algorithm, etc.) to perform energy efficiency optimization for the K power terminals. By using the background factor as the objective function, the main factors affecting the energy efficiency of the power terminal can be more accurately identified, and then a more targeted optimization plan can be formulated.

[0074] Optionally, the K energy efficiency optimization background factors form the above objective function through a weighted fusion strategy, where the fusion weight of each energy efficiency optimization background factor is determined based on the importance of the corresponding power terminal, so as to ensure that the optimized objective function can reasonably reflect the energy efficiency level and priority of the power terminal under different operating conditions. Among them, the importance of the power terminal is defined by factors such as the corresponding power grid topological location, load level, redundancy and standby, and historical operating performance.

[0075] In summary, the method for optimizing the energy efficiency of power terminal equipment for smart grids provided by the present invention has the following technical effects:

[0076] Obtain the distribution network information of power terminals in the target power grid, where the power terminal distribution network consists of K power terminals and their corresponding installation locations, and K is an integer not less than 1; based on the sensor integration array installed on each power terminal, extract the operation data of each power terminal within the specified monitoring window to obtain K sets of monitored operation data, where each set contains all operation index data within the monitoring window; perform centralized value analysis on the K sets of monitored operation data to generate K sets of explicit operation centralized values; traverse the K power terminals one by one, extract their long-time series operation data, and obtain K sets of long-time series operation data; use the K sets of long-time series operation data to carry out data segmentation and iterative analysis to identify K implicit operation factors; combine the distribution network of power terminals and their installation locations, implement near-neighbor terminal sampling, and perform correlation analysis based on the sampling results and the K implicit operation factors to determine K background factors for energy efficiency optimization; finally, based on the K background factors for energy efficiency optimization and the K sets of explicit operation centralized values, carry out energy efficiency optimization for the K power terminals and output K corresponding energy efficiency optimization schemes. Thus, the technical effects of improving equipment collaboration and operating condition adaptability and improving energy efficiency performance are achieved.

[0077] Embodiment 2

[0078] Figure 2 It is a schematic structural diagram of the device for optimizing the energy efficiency of power terminal equipment for the smart grid of the present invention. For example, Figure 1 In the present invention, the flow schematic diagram of the method for optimizing the energy efficiency of power terminal equipment for the smart grid can be implemented by a structure as shown in Figure 2 shown.

[0079] Based on the same concept as the method for optimizing the energy efficiency of power terminal equipment for the smart grid in the above embodiment, the device for optimizing the energy efficiency of power terminal equipment for the smart grid provided by the present invention further includes:

[0080] The distribution network acquisition module 11 is used to acquire the power terminal distribution network of the target power grid, where the power terminal distribution network includes K power terminals and K terminal installation locations, and K is an integer greater than or equal to 1.

[0081] The operation data extraction module 12 is used to extract the operation data of the K power terminals within the preset monitoring window based on the sensor integration array arranged on each power terminal, and obtain K sets of monitored operation data, where each set of monitored operation data includes all operation data of an operation index within the preset monitoring window.

[0082] The centralized value analysis module 13 is used to perform centralized value analysis on the K sets of monitored operation data to obtain K sets of explicit operation centralized values.

[0083] The long-time sequence extraction module 14 is used to traverse the K power terminals to extract long-time sequence operation data, and obtain K long-time sequence operation data sets.

[0084] The segmented iterative analysis module 15 is used to perform data segmented iterative analysis based on the K long-time sequence operation data sets to determine K implicit operation factors.

[0085] The background factor positioning module 16 is used to perform neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and perform correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors.

[0086] The optimization decision module 17 is used to perform energy efficiency optimization on the K power terminals according to the K energy efficiency optimization background factors and the K explicit operation concentration value sets, and obtain K energy efficiency optimization schemes.

[0087] Among them, the sensor integration array in the operation data extraction module 12 includes a voltage sensor, a current sensor, a power sensor, a temperature sensor, and a vibration sensor.

[0088] In some embodiments, the concentration value analysis module 13 includes

[0089] The mean value calculation unit is used to extract the first monitoring operation data set from the K monitoring operation data sets, and calculate the mean value of the first monitoring operation data set to obtain the first monitoring operation mean value.

[0090] The concentration density search unit is used to search in the first monitoring operation data set according to a preset concentration bandwidth with the first monitoring operation mean value as the search center to determine the first search monitoring operation data.

[0091] The concentration density comparison and direction determination unit is used to calculate the concentration densities of the first monitoring operation mean value and the first search monitoring operation data respectively. When the concentration density of the first search monitoring operation data is greater than or equal to the concentration density of the first monitoring operation mean value, obtain the first search direction.

[0092] The search update unit is used to update the first search monitoring operation data as the search center, based on the first search direction, and search in the first monitoring operation data set according to a preset concentration bandwidth until the concentration density difference between two adjacent searches is less than or equal to a preset concentration density difference, stop the search, use the search center corresponding to the maximum concentration density during the search as the target search center, and use the first monitoring operation data corresponding to the target search center as the first explicit operation concentration value set.

[0093] A traversal search unit for performing centralized value analysis on the K monitoring operation data sets according to a preset centralized bandwidth to obtain the K explicit operation centralized value sets.

[0094] In some embodiments, in the centralized value analysis module 13, the first search direction is the direction of the first search monitoring operation data relative to the first monitoring operation mean value.

[0095] In some implementation manners, the segmented iterative analysis module 15 includes

[0096] A long-time-series data segmentation unit for segmenting the K long-time-series operation data sets in a direction away from the preset monitoring window according to the time length of the preset monitoring window to obtain K long-time-series segmented operation data clusters.

[0097] A fluctuation variance calculation unit for respectively calculating the fluctuation variances of the K long-time-series segmented operation data clusters to obtain K segmented fluctuation variance clusters.

[0098] An implicit operation factor determination unit for performing weighted calculation on the K segmented fluctuation variance clusters according to a preset weight distribution scheme to obtain the K implicit operation factors.

[0099] In some embodiments, the background factor positioning module 16 includes:

[0100] A first-order neighbor terminal sampling unit for performing first-order neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions to obtain K first-order neighbor terminal sets.

[0101] A second-order neighbor terminal sampling unit for performing second-order neighbor terminal sampling in the power terminal distribution network based on the K first-order neighbor terminal sets and the K terminal layout positions to obtain K second-order neighbor terminal sets.

[0102] An implicit operation factor matching unit for matching the K implicit operation factors with the K first-order neighbor terminal sets and the K second-order neighbor terminal sets as indexes to obtain K first-order neighbor implicit operation factor sets and K second-order neighbor implicit operation factor sets.

[0103] An association weighted analysis unit for performing association weighted analysis on the K first-order neighbor implicit operation factor sets, the K second-order neighbor implicit operation factor sets and the K implicit operation factors according to the distances to the K power terminals to obtain the K energy efficiency optimization background factors.

[0104] In some implementations, the first-order neighboring terminal in the background factor positioning module 16 is a terminal that is on the same line as the power terminal and adjacent in position in the power terminal distribution network. The second-order neighboring terminal is a terminal that is on the same line as the first-order neighboring terminal and adjacent in position in the power terminal distribution network.

[0105] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the foregoing Embodiment 1 are equally applicable to the power terminal device energy efficiency optimization device for smart grid described in Embodiment 2. For the sake of brevity of the specification, no further elaboration is made here.

[0106] It should be understood that the disclosed embodiments of the present invention and the above descriptions enable those skilled in the art to implement the present invention using the present invention. At the same time, the present invention is not limited to the part of the embodiments mentioned above. It should be understood that those of ordinary skill in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included within the protection scope of the present invention.

Claims

1. An energy efficiency optimization method for power terminal equipment in a smart grid, characterized in that, The method includes: Obtaining the power terminal distribution network of the target power grid, where the power terminal distribution network includes K power terminals and K terminal layout positions, and K is an integer greater than or equal to 1; Based on the sensor integration array deployed on each power terminal, extracting the operation data of the K power terminals in a preset monitoring window to obtain K monitoring operation data sets, where each monitoring operation data set includes all the operation data of an operation index within the preset monitoring window; Performing centralized value analysis on the K monitoring operation data sets to obtain K explicit operation centralized value sets; Traversing the K power terminals to extract long-time series operation data to obtain K long-time series operation data sets; Based on the K long-time series operation data sets, performing data segmentation iterative analysis to determine K implicit operation factors. The implicit operation factors refer to comprehensive characteristic indicators extracted after segmented fluctuation variance analysis and weight allocation in long-time series data, and are used to reflect the potential state or trend of the power terminal during long-term operation; Performing neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and performing correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors; Performing energy efficiency optimization on the K power terminals according to the K energy efficiency optimization background factors and the K explicit operation centralized value sets to obtain K energy efficiency optimization schemes; Performing centralized value analysis on the K monitoring operation data sets to obtain K explicit operation centralized value sets, including: Extracting the first monitoring operation data set from the K monitoring operation data sets and calculating the mean value of the first monitoring operation data set to obtain the first monitoring operation mean value; Using the first monitoring operation mean value as the search center, searching in the first monitoring operation data set according to a preset centralized bandwidth to determine the first search monitoring operation data; Calculating the centralized density of the first monitoring operation mean value and the first search monitoring operation data respectively. When the centralized density of the first search monitoring operation data is greater than or equal to the centralized density of the first monitoring operation mean value, obtaining the first search direction; Updating the first search monitoring operation data as the search center, based on the first search direction, and searching in the first monitoring operation data set according to a preset centralized bandwidth until the difference in centralized density between two adjacent searches is less than or equal to a preset centralized density difference, stopping the search, taking the search center corresponding to the maximum centralized density during the search process as the target search center, and taking the first monitoring operation data corresponding to the target search center as the first explicit operation centralized value set; Performing centralized value analysis on the K monitoring operation data sets according to a preset centralized bandwidth to obtain the K explicit operation centralized value sets.

2. The method according to claim 1, characterized in that, The sensor integration array includes a voltage sensor, a current sensor, a power sensor, a temperature sensor, and a vibration sensor.

3. The method according to claim 1, wherein The first search direction is the direction of the first search monitoring operation data relative to the first monitoring operation mean value.

4. The method according to claim 1, wherein Performing data segmentation iterative analysis based on the K sets of long-time series operation data to determine K implicit operation factors, including: Segmenting the K sets of long-time series operation data in a direction away from the preset monitoring window according to the time length of the preset monitoring window to obtain K sets of long-time series segmented operation data clusters; Calculating the fluctuation variances of the K sets of long-time series segmented operation data clusters respectively to obtain K sets of segmented fluctuation variance clusters; Performing weighted calculation on the K sets of segmented fluctuation variance clusters according to a preset weight distribution scheme to obtain the K implicit operation factors.

5. The method according to claim 1, wherein Performing nearest neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and performing correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors, including: Performing first-order nearest neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions to obtain K sets of first-order nearest neighbor terminals; Performing second-order nearest neighbor terminal sampling in the power terminal distribution network based on the K sets of first-order nearest neighbor terminals and the K terminal layout positions to obtain K sets of second-order nearest neighbor terminals; Using the K sets of first-order nearest neighbor terminals and the K sets of second-order nearest neighbor terminals as indexes to match the K implicit operation factors to obtain K sets of first-order nearest neighbor implicit operation factor sets and K sets of second-order nearest neighbor implicit operation factor sets; Performing correlation weighted analysis on the K sets of first-order nearest neighbor implicit operation factor sets, the K sets of second-order nearest neighbor implicit operation factor sets and the K implicit operation factors according to the distances to the K power terminals to obtain the K energy efficiency optimization background factors.

6. The method according to claim 5, characterized in that Including: The first-order nearest neighbor terminal is a terminal that is on the same line as the power terminal and adjacent in position in the power terminal distribution network; The second-order nearest neighbor terminal is a terminal that is on the same line as the first-order nearest neighbor terminal and adjacent in position in the power terminal distribution network.

7. An energy efficiency optimization device for power terminal equipment in a smart grid, characterized in that, The device is used to execute the method for optimizing the energy efficiency of power terminal equipment for a smart grid according to any one of claims 1-6. The device includes: A distribution network acquisition module, which is used to acquire the power terminal distribution network of the target grid, where the power terminal distribution network includes K power terminals and K terminal layout positions, and K is an integer greater than or equal to 1; An operation data extraction module, which is used to extract the operation data of the K power terminals in a preset monitoring window based on the sensor integration array arranged on each power terminal to obtain K sets of monitored operation data, where each set of monitored operation data includes all operation data of an operation index within the preset monitoring window; A central value analysis module, which is used to perform central value analysis on the K sets of monitored operation data to obtain K sets of explicit operation central value sets; A long-time series extraction module, which is used to traverse the K power terminals to extract long-time series operation data to obtain K sets of long-time series operation data; A segmented iterative analysis module, which is used to perform data segmented iterative analysis based on the K long-time series operation data sets to determine K implicit operation factors; A background factor positioning module, which is used to perform near-neighbor terminal sampling on the K power terminals based on the power terminal distribution network and the K terminal layout positions, and perform correlation analysis according to the sampling results and the K implicit operation factors to determine K energy efficiency optimization background factors; An optimization decision module, which is used to perform energy efficiency optimization on the K power terminals according to the K energy efficiency optimization background factors and the K explicit operation centralized value sets to obtain K energy efficiency optimization solutions.

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