Disclosure of Invention
The invention provides an automatic electricity-saving monitoring method and system for electric energy of an electricity-saving cabinet, which are used for solving the existing problems.
The invention relates to an automatic electricity-saving monitoring method and system for electric energy of an electricity-saving cabinet, which adopts the following technical scheme:
The embodiment of the invention provides an automatic electricity-saving monitoring method for electric energy of an electricity-saving cabinet, which comprises the following steps:
Acquiring a current data graph constructed by current data; wherein the horizontal axis of the current data graph is time, and the vertical axis is current data;
Acquiring an analysis window of each current data point in the current data diagram, and obtaining the local fluctuation degree of each current data point in the current data diagram according to the slope of the adjacent current data point connecting line of each current data point in the analysis window and the current data value of each current data point;
Obtaining the local noise possibility of each current data point according to the difference of the local fluctuation degrees of all the current data points in the current data diagram;
taking an analysis window where a current data point corresponding to the minimum local noise possibility in the current data diagram is located as a reference data segment, and obtaining the noise influence degree of each current data point according to the local noise possibility of each current data point in the current data diagram, the slope of a connecting line of adjacent current data points and the slope of a connecting line of adjacent current data points in the reference data segment;
Obtaining the size of a filtering window of each current data point according to the noise influence degree of each current data point in the current data diagram;
And according to the size of the filtering window of each current data point in the current data diagram, the automatic electricity saving of the electricity saving cabinet is completed.
Further, the analysis window for obtaining each current data point in the current data graph comprises the following specific steps:
In the current data diagram, an analysis window of any one current data point with the time range of S is established by taking the time of any one current data point as the center, and S is a preset time range.
Further, the step of obtaining the local fluctuation degree of each current data point in the current data graph according to the slope of the adjacent current data point connection line of each current data point in the current data graph and the current data value of each current data point in the analysis window comprises the following specific steps:
Obtaining the slope characteristics of the adjacent current data point connecting lines in the analysis window where each current data point in the current data diagram is located according to the slope of the adjacent current data point connecting lines in the analysis window of each current data point in the current data diagram; and obtaining the local fluctuation degree of each current data point in the current data diagram according to the slope characteristic of the connecting line of the adjacent current data points in the analysis window where each current data point is located in the current data diagram and the current data value of each current data point.
Further, according to the slope characteristics of the connection lines of adjacent current data points in the analysis window where each current data point is located in the current data diagram and the current data value of each current data point, the local fluctuation degree of each current data point in the current data diagram is obtained, and the corresponding specific calculation formula is as follows:
In the middle ofRepresenting the local fluctuation degree of the ith current data point in the current data graph; /(I)Representing the number of current data points contained in an analysis window where the ith data point in the current data diagram is located; /(I)Representing the slope of the line connecting the s-th and s+1-th current data points in the analysis window where the i-th current data point is located in the current data diagram; /(I)Representing the average value of the slope of the connecting line of the adjacent current data points in the analysis window where the ith current data point is positioned in the current data diagram; /(I)Representing the/>, within the analysis window, of the current data graph, where the ith current data point is locatedA current data value for a data point; /(I)Representing the average value of current data values of all current data points in an analysis window where the ith data point in the current data diagram is located,/>Representing the slope characteristics of the connection line of adjacent current data points in the analysis window where the ith current data point in the current data diagram is located,/>As a function of absolute value.
Further, according to the difference of the local fluctuation degrees of all the current data points in the current data graph, the local noise possibility of each current data point is obtained, and the corresponding specific calculation formula is as follows:
In the middle ofRepresenting the local noise probability of the ith current data point in the current data plot; /(I)Representing the local fluctuation degree of the ith current data point in the current data graph; /(I)A mean value representing the local fluctuation degree of all current data points in the current data graph; /(I)Representing the local fluctuation degree of an (r) data point except an (i) current data point in the current data graph; /(I)Standard deviation representing the degree of local fluctuation of all current data points in the current data graph; n represents the number of current data points in the current data plot; /(I)Representing a linear normalization function,/>As a function of absolute value.
Further, the obtaining the noise influence degree of each current data point according to the local noise probability of each current data point in the current data diagram, the slope of the adjacent current data point connecting line and the slope of the adjacent current data point connecting line in the reference data section comprises the following specific steps:
obtaining a noise coefficient of each current data point in the current data diagram according to the slope of the adjacent current data point connecting line and the slope of the adjacent current data point connecting line in the reference data section; and obtaining the noise influence degree of each current data point according to the noise coefficient of each current data point in the current data diagram and the local noise probability of each current data point in the current data diagram.
Further, according to the noise coefficient of each current data point in the current data diagram and the local noise probability of each current data point in the current data diagram, the noise influence degree of each current data point is obtained, and the corresponding specific calculation formula is as follows:
In the middle ofRepresenting the noise influence degree of the ith current data point in the current data graph; /(I)Representing the slope of the line connecting the ith and the (i-1) th current data points in the current data graph; /(I)Representing the slope of the ith and (i+1) th current data point lines in the current data graph; /(I)Representing the average value of the slope of adjacent current data point lines in a reference data segment in the current data graph; /(I)Representing the local noise probability of the ith current data point in the current data plot; e represents a natural constant; /(I)Representing a number of current data points in the current data graph that are the same as the current data value of the i-th current data point; noise figure representing the ith current data point in the current data graph,/>As a function of absolute value.
Further, according to the noise influence degree of each current data point in the current data diagram, the size of the filtering window of each current data point is obtained, and the corresponding specific calculation formula is as follows:
In the middle ofA filter window size representing an ith current data point in the current data plot; /(I)Representing the noise influence degree of the ith current data point in the current data graph; /(I)Representing the number of current data points contained in an analysis window where the ith data point in the current data diagram is located; /(I)Representing the/>, within the analysis window, of the current data graph, where the ith current data point is locatedThe degree of noise impact of the data points; a is a preset first parameter, B is a preset second parameter, and C is a preset third parameter; representing a linear normalization function; /(I)Representing a round-up function.
Further, according to the size of the filtering window of each current data point in the current data diagram, the automatic power saving of the power saving cabinet is completed, and the method comprises the following specific steps:
according to the size of a filtering window of each current data point in the current data diagram, performing filtering operation on current data in the current data diagram by using a self-adaptive mean value filtering algorithm to obtain a filtered current data time sequence;
And inputting the filtered current data time sequence into a PLC controller, and outputting a control instruction of the electric energy of the power-saving cabinet.
The invention also provides an automatic power-saving monitoring system for the power-saving cabinet, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program stored in the memory so as to realize the steps of the automatic power-saving monitoring method for the power-saving cabinet.
The technical scheme of the invention has the beneficial effects that: the method comprises the steps of obtaining a current data graph constructed by current data and an analysis window of each current data point, obtaining the local fluctuation degree of each current data point in the current data graph according to the characteristics of each current data point in the analysis window in the current data graph, thereby improving the analysis accuracy, obtaining the local noise possibility of each current data point according to the difference of the local fluctuation degrees of all current data points, obtaining the noise influence degree of each current data point, helping to accurately remove noise, finally obtaining the size of a filtering window of each current data point, guaranteeing the accuracy of a final filtering result, and improving the accuracy of monitoring and early warning the electricity consumption of the electricity-saving cabinet in a power grid.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the method and system for automatically monitoring the power saving of the power saving cabinet according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a power-saving cabinet electric energy automatic power-saving monitoring method and a specific scheme of a system, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a method for automatically monitoring power saving of a power saving cabinet according to an embodiment of the invention is shown, the method includes the following steps:
step S001: acquiring a current data graph constructed by current data; wherein, the horizontal axis of the current data graph is time, and the vertical axis is current data.
The embodiment mainly aims to remove noise of collected current data in the process of monitoring the power consumption of the power grid by the power saving cabinet, so that the accuracy of monitoring the power consumption of the power grid by the power saving cabinet is improved. Therefore, the high-precision current sensor arranged inside the power-saving cabinet is required to collect the current data in the power grid, the time for collecting the current data is 10 minutes, the frequency for collecting the data is 0.02 seconds, and the current data is described by way of example, other values can be set in other embodiments, and the embodiment is not limited. And constructing a current data graph according to the acquired current data by taking time as a horizontal axis and taking the current data as a vertical axis. FIG. 2 is a current data diagram of the power saving cabinet; the horizontal axis is time, which is in seconds; the vertical axis is current in a (amperes).
Step S002: and acquiring an analysis window of each current data point in the current data graph, and obtaining the local fluctuation degree of each current data point in the current data graph according to the slope of the adjacent current data point connecting line of each current data point in the current data graph and the current data value of each current data point in the analysis window.
When the electricity-saving cabinet monitors the electricity consumption condition in the electricity consumption network, due to load diversity, linear load and nonlinear load can exist in the connected power network, and the nonlinear load can cause instantaneous current in monitored current data, so that noise or fluctuation can exist in the collected current when the electricity-saving cabinet monitors the electricity consumption condition, and the accuracy of the electricity-saving cabinet on the monitored electricity consumption monitoring result of the electricity consumption in the power network is affected. When the collected current is denoised, noise current is eliminated as much as possible, real current data is reserved to a greater extent, so that the monitoring precision and accuracy of the power-saving cabinet can be ensured, and the power-saving efficiency is improved. Since transient voltages are typically generated by nonlinear consumers when they are connected to or disconnected from the power grid, they may cause local fluctuations in the current data collected. The relative difference in the characteristics of the noise data relative to the normal data is relatively apparent. In the current data graph, an analysis window with a time range of S is established with the time of any current data point as the center, so as to obtain the analysis window of any data point, S is a preset time range, the value of S in this embodiment is 11, which is described as an example, and other values can be set in other embodiments, which is not limited. The local extent of fluctuation for each current data point is calculated as follows:
In the middle ofRepresenting the local fluctuation degree of the ith current data point in the current data graph; /(I)Representing the number of current data points contained in an analysis window where the ith data point in the current data diagram is located; /(I)Representing the slope of the line connecting the s-th and s+1-th current data points in the analysis window where the i-th current data point is located in the current data diagram; /(I)Representing the average value of the slope of the connecting line of the adjacent current data points in the analysis window where the ith current data point is positioned in the current data diagram; /(I)Representing the/>, within the analysis window, of the current data graph, where the ith current data point is locatedA current data value for a data point; /(I)Representing the average value of current data values of all current data points in an analysis window where the ith data point in the current data diagram is located,/>The slope characteristic of the connecting line of the adjacent current data points in the analysis window where the ith current data point is positioned in the current data graph is represented, the larger the value is, the smoother the current data change in the section is, and the larger the local fluctuation degree of the section of data points is; /(I)Representing the absolute value of the difference between the current value of each data point in the analysis window of the ith data point and the average value of the current data points in the analysis window of the ith data point in the current data graph, wherein the larger the absolute value is, the larger the overall difference of the current data in the section is, the larger the fluctuation degree is, and the greater the fluctuation degree isAs a function of absolute value.
Step S003: and obtaining the local noise probability of each current data point according to the difference of the local fluctuation degrees of all the current data points in the current data graph.
When the current data has local noise, the local fluctuation degree of the noise data point is greatly different from that of other data points. Thus, the difference between the local fluctuation degree of each data point and the local fluctuation degree of other data points is used to obtain the possibility of noise existing locally in each data point, and the calculation formula is as follows:
In the middle ofRepresenting the local noise probability of the ith current data point in the current data plot; /(I)Representing the local fluctuation degree of the ith current data point in the current data graph; /(I)A mean value representing the local fluctuation degree of all current data points in the current data graph; /(I)Representing the local fluctuation degree of an (r) data point except an (i) current data point in the current data graph; /(I)Standard deviation representing the degree of local fluctuation of all current data points in the current data graph; n represents the number of current data points in the current data plot; /(I)Representing a linear normalization function, normalizing the results of the probability of local noise present at the ith current data point to within the [0,1] interval,/>As a function of absolute value.
The greater the degree of deviation of the local fluctuation level representing the ith current data point relative to the overall fluctuation level of the acquired current data, the greater the likelihood that the local where the ith current data point is located will be disturbed by noise.The difference between the local fluctuation degree of the ith current data point and the local fluctuation degree of the rest data point is shown, and the larger the value is, the greater the possibility that noise interference exists in the local part of the ith current data point is.
Step S004: and taking an analysis window of the current data point corresponding to the minimum local noise possibility in the current data diagram as a reference data section, and obtaining the noise influence degree of each current data point according to the local noise possibility of each current data point in the current data diagram, the slope of the adjacent current data point connecting line and the slope of the adjacent current data point connecting line in the reference data section.
In the current data diagram, an analysis window where a current data point corresponding to the minimum local noise possibility is located is taken as a reference data segment, when a plurality of current data points corresponding to the minimum local noise possibility exist, one current data point is selected, then the noise degree of each current data point is calculated, and the window size of each data point when filtering is obtained through the noise degree of each data point. The noise impact level for each current data point is calculated as follows:
In the middle ofRepresenting the noise influence degree of the ith current data point in the current data graph; /(I)Representing the slope of the line connecting the ith and the (i-1) th current data points in the current data graph; /(I)Representing the slope of the ith and (i+1) th current data point lines in the current data graph; /(I)Representing the average value of the slope of adjacent current data point lines in a reference data segment in the current data graph; /(I)Representing the probability of local noise of the ith current data point in the current data graph, wherein the larger the value is, the greater the possible influence degree of the local noise on the ith current data point is, and the greater the possible influence degree of the noise on the ith current data point is; e represents a natural constant; /(I)Representing the same number of current data points in the current data graph as the current data value of the ith current data point, because noise data such as instantaneous current is not the normal change of the current data in normal use in the power grid, the size is relatively random, and therefore the more the number of current data points with the same data value as the ith current data point is, the more the normal change of the current data in normal use is possible, the/>, theThe larger the value is, the greater the noise influence degree of the ith data point is; /(I)The noise coefficient of the ith current data point in the current data diagram is represented by the calculation process that the average value of the sum of the absolute values of the slopes corresponding to the ith data point and the front and back adjacent data points in the current data diagram and the absolute value of the difference value of the average value of the slopes of the connecting lines of the adjacent current data points in the reference data section are calculated, wherein the greater the value is, the greater the degree of influence of noise on the ith data point is, iAs a function of absolute value.
Step S005: and obtaining the size of a filtering window of each current data point according to the noise influence degree of each current data point in the current data diagram.
By obtaining the noise influence degree of each data point, when filtering the data point, the selected filter window size is calculated as follows:
In the middle ofThe size of the filtering window representing the ith current data point in the current data diagram, i.e. the total selection/>, centered around the ith data point when filtering itFiltering the data points; /(I)The noise influence degree of the ith current data point in the current data graph is represented, the larger the value is, the larger the noise influence degree of the ith data point is indicated, and the larger the filter window selected by the ith data point is; /(I)Representing the number of current data points contained in an analysis window where the ith data point in the current data diagram is located; /(I)Representing the/>, within the analysis window, of the current data graph, where the ith current data point is locatedThe degree of noise impact of the data points; a is a preset first parameter, B is a preset second parameter, and C is a preset third parameter, in this embodiment, a=2, b=50, and c=1 are described as examples, and other values may be set in other embodiments, which are not limited in this embodiment, and parameters a and C are set so that the filter window size is an odd window; /(I)Representing a linear normalization function; /(I)Representing a round-up function. /(I)Representing the mean value of the summation of the noise impact levels of each current data point in the analysis window where the ith current data point is located in the current data graph, the greater the value is to indicate the greater the local noise impact level of the ith current data point, the greater the window selected when filtering the ith current data point.
Step S006: and according to the size of the filtering window of each current data point in the current data diagram, the automatic electricity saving of the electricity saving cabinet is completed.
And according to the size of a filtering window of each current data point in the current data diagram, performing filtering operation on the current data in the current data diagram by using a self-adaptive mean value filtering algorithm to obtain a filtered current data time sequence, inputting the filtered current data time sequence into a PLC (programmable logic controller), and outputting a control instruction of the electric energy of the power-saving cabinet. The adaptive mean filtering algorithm and the PLC controller are known techniques, and specific methods are not described herein.
What needs to be described is: the method comprises the steps of obtaining the size of a filtering window when current data are denoised, denoising the current data by utilizing self-adaptive mean value filtering, obtaining information such as an energy consumption mode, peak-valley load condition and the like of access equipment in a power grid by utilizing a statistical method or other methods, then formulating specific power saving strategies, for example, carrying out early warning prompts of different grades according to load conditions of different time periods, setting on-off time of other electric equipment, converting the power saving strategies into control instructions, sending the control instructions to corresponding equipment, carrying out operations such as on-off operation on the electric equipment by utilizing equipment such as a centralized control system, a Programmable Logic Controller (PLC) and the like, wherein the control instructions are the on-off instruction and the on-off instruction, and realizing the purpose of automatic power saving.
The present invention has been completed.
The invention also provides an automatic power-saving monitoring system for the power-saving cabinet, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program stored in the memory so as to realize the steps of the automatic power-saving monitoring method for the power-saving cabinet.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.