CN118611274B - Monitoring method and system based on electric power energy management system - Google Patents

Monitoring method and system based on electric power energy management system Download PDF

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CN118611274B
CN118611274B CN202411087125.2A CN202411087125A CN118611274B CN 118611274 B CN118611274 B CN 118611274B CN 202411087125 A CN202411087125 A CN 202411087125A CN 118611274 B CN118611274 B CN 118611274B
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刘全东
张宁
魏丽
蒋慧玲
雷震
罗继东
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Inner Mongolia Dongchuang Power Sales Co ltd
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    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network
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    • H—ELECTRICITY
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Abstract

The invention discloses a monitoring method and a monitoring system based on an electric power energy management system, and relates to the technical field of electric power system monitoring, wherein the monitoring method comprises the steps of collecting electric power data and determining the position of a load node in an electric power energy system through a time domain reflectometer; monitoring load nodes and extracting load characteristics by using a non-invasive load monitoring technology; according to the load characteristics, a machine learning algorithm is constructed to analyze the state of the electric power energy system for early warning, and the electric power energy system is optimized and adjusted based on a multi-layer perceptron neural network; and constructing a user interaction interface to display the power data in real time. According to the invention, the power data is collected, the position of the load node in the power energy system is calculated based on the time domain reflectometer, the accuracy and the flexibility of load identification are improved, the load characteristics of the load node are extracted by using the recursion diagram and the deep vision processing network, the correlation and the accuracy of the characteristics are improved, the early warning and the adjustment of the power energy system are synchronously carried out based on the load characteristics, and the risk resistance of the power energy system is enhanced.

Description

Monitoring method and system based on electric power energy management system
Technical Field
The invention relates to the technical field of power system monitoring, in particular to a monitoring method and system based on a power energy management system.
Background
With the increasing global energy demand and increasing wide application of renewable energy sources, an electric power energy management system gradually becomes an important tool for optimizing energy use and improving energy utilization efficiency, in recent years, the development of smart power grids and the internet of things technology is that the electric power energy management system is injected with new vigor, so that the intelligent power grid and the internet of things technology can perform electric power monitoring and management more intelligently, automatically and efficiently, the traditional electric power management method mainly depends on wired monitoring and manual control, lacks instantaneity and accuracy, is difficult to meet the demand of modern energy management, provides a new solution for the electric power energy management system with the introduction of a machine learning algorithm, but the prior art still has the defects that the traditional method is low in efficiency and easy to generate errors in determining the position of a load node in the electric power energy system, is easy to suffer from noise interference when complex load data are processed, and is low in recognition accuracy.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems occurring in the conventional monitoring method and system based on the electric power energy management system.
Therefore, the problem to be solved by the invention is that the traditional method is low in efficiency and easy to generate errors in the aspect of determining the position of the load node in the electric power energy system, and is easy to be interfered by noise when complex load data are processed, so that the identification precision is not high.
In order to solve the technical problems, the invention provides the following technical scheme: a monitoring method based on an electric power energy management system comprises the steps of collecting electric power data and determining the position of a load node in the electric power energy system through a time domain reflectometer; monitoring load nodes and extracting load characteristics by using a non-invasive load monitoring technology; according to the load characteristics, a machine learning algorithm is constructed to analyze the state of the electric power energy system for early warning, and the electric power energy system is optimized and adjusted based on a multi-layer perceptron neural network; and constructing a user interaction interface to display the power data in real time.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the step of collecting power data, the step of determining the position of a load node in the power energy system through a time domain reflectometer is to collect the power data by arranging a sensor in the power energy system, collect power line parameters in the power energy system, and calculate a first scale factor k and a first adjustment factor k , according to the power line parameters:
；
where s is the length of the power line and b is the total width of the power line;
calculating a second scaling factor k 1 and a second adjustment factor ：
；
Wherein h is the thickness of the power line;
Calculating a first scaling factor k and a first adjustment factor k ,, a second scaling factor k 1 and a second adjustment factor Is the first type of elliptic integral of (a):
；
Where x is input data and includes a first scaling factor k and a first adjustment factor k , and a second scaling factor k 1 and a second adjustment factor ，As an integral variable, the regional range is 0 to pi/2;
Calculating effective dielectric constant of power line according to first-class elliptic integral ：
；
Wherein the method comprises the steps ofThe relative dielectric constant of the material of the power circuit;
According to the effective dielectric constant Calculating the characteristic impedance Z 0 of the power line:
；
Under the no-load state of the electric power energy system, using TDR equipment to inject a high-frequency pulse signal into an electric power line as an incident voltage signal, collecting a reflected voltage signal, taking the ratio of the reflected voltage signal to the incident voltage signal as a no-load reflection coefficient, and calculating the no-load impedance of the electric power energy system based on the no-load reflection coefficient:
；
where Z r is the no-load impedance, Is the no-load reflection coefficient;
Repeatedly operating in a state that the electric power energy system is connected to a load to obtain the load impedance Z y of the electric power energy system, and calculating an impedance change coefficient S according to the load impedance and the no-load impedance:
；
Selecting power lines at different positions in an electric power energy system, calculating to obtain impedance change coefficients by using TDR equipment, recording the position tested each time and the time difference t between an incident voltage signal and a reflected voltage signal, forming a line graph according to the impedance change coefficients at all positions from small to large, identifying peaks in the line graph, extracting the test position of the impedance change coefficient at the peak position, calculating the distance between a load node and the test position according to the time difference t and the signal speed, and further determining the position of the load node.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the non-invasive load monitoring technology is used for monitoring load nodes and extracting load characteristics, namely, after the positions of the load nodes are determined, power data of the load nodes are collected in real time through a sensor to obtain current I k and voltage V k which are sampled in real time, the pearson correlation coefficients of the current I k and the voltage V k are calculated, and the scaling coefficient is calculated:
；
Wherein the method comprises the steps of For scaling factor, cor (I k,Vk) is the pearson correlation of current I k and voltage V k;
constructing an adaptive scaling recursion model A according to the scaling coefficient:
；
Wherein I k (I) and I k (j) are the I-th and k-th currents, respectively, L is the total number of current samples;
And obtaining an adaptive scaling recursion diagram through the adaptive scaling recursion model A, identifying the adaptive scaling recursion diagram by using a depth vision processing network Swin-transform, and extracting load characteristics of load nodes in the adaptive scaling recursion diagram, wherein the load characteristics comprise average power consumption, average running time and daily running times.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the construction of a machine learning algorithm according to the load characteristics analyzes the state of the electric power energy system to perform early warning, namely, a convolutional neural network is constructed, and an input format is defined as a load characteristic format;
Collecting historical power data in a power energy system, extracting historical load characteristics, inputting the historical load characteristics as a training set into a convolutional neural network for training, and optimizing parameters of the convolutional neural network;
Inputting the load characteristics extracted through the real-time data of the load nodes into an optimized convolutional neural network, and outputting the state of an electric power energy system, including normal and abnormal states;
if the state of the electric power energy system is normal, monitoring is kept;
and if the state of the electric power energy system is abnormal, early warning is carried out.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the optimization adjustment of the electric power energy system based on the multi-layer perceptron neural network means that the multi-layer perceptron neural network is constructed,
Collecting historical load characteristics of all load nodes in the electric power energy network, training the multi-layer perceptron neural network, optimizing the target to minimize the operation energy consumption, and optimizing the parameters of the multi-layer perceptron neural network based on forward propagation, backward propagation and minimized loss function;
and collecting load characteristics of all load nodes in the electric power energy network, inputting the load characteristics into the trained multi-layer perceptron neural network, outputting the optimal running time and the optimal running load of each load node, and adjusting the electric power energy system based on the optimal running time and the optimal running load.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the sensor comprises a current sensor, a voltage sensor, an energy consumption sensor and a GPS sensor, wherein the current sensor, the voltage sensor, the energy consumption sensor and the GPS sensor are distributed into groups, and are deployed in an equidistant manner in the electric power energy system.
As a preferable scheme of the monitoring method based on the electric power energy management system, the invention comprises the following steps: the construction of the user interaction interface to display the power data in real time means that the construction of the user interface displays the power data collected by the sensor, an interaction function is added for the user interface, the user is allowed to customize the display content of the power data, and the high-brightness identification of the load node in the power energy system is synchronously displayed in the user interface.
It is another object of the present invention to provide a monitoring system based on an electric power energy management system, which includes a data collection module for deploying sensors in the electric power energy system to collect electric power data;
The load identification module is used for identifying load nodes according to the power data and calculating the positions of the load nodes;
the characteristic extraction module is used for extracting load characteristics according to the power data of the load nodes;
The early warning optimization module is used for analyzing and early warning the state of the electric power energy system according to the load characteristics and synchronously optimizing and adjusting the electric power energy system;
And the display module is used for displaying the power data and the load nodes to a user.
A computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of the monitoring method based on the electric power energy management system.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described monitoring method based on an electrical energy management system.
The invention has the beneficial effects that: according to the invention, the power data is collected, the position of the load node in the power energy system is calculated based on the time domain reflectometer, the accuracy and the flexibility of load identification are improved, the load characteristics of the load node are extracted by using the recursion diagram and the deep vision processing network, the correlation and the accuracy of the characteristics are improved, the early warning and the adjustment of the power energy system are synchronously carried out based on the load characteristics, and the risk resistance of the power energy system is enhanced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a monitoring method based on an electric power energy management system.
Fig. 2 is a schematic structural diagram of a monitoring system based on an electric power energy management system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Examples
Referring to fig. 1, a first embodiment of the present invention provides a monitoring method based on an electric power energy management system, the monitoring method based on the electric power energy management system includes,
S1, collecting electric power data, and determining the position of a load node in an electric power energy system through a time domain reflectometer;
Specifically, collecting power data, determining the position of a load node in the power energy system through a time domain reflectometer refers to collecting the power data by deploying a sensor in the power energy system, collecting power line parameters in the power energy system, and calculating a first scale factor k and a first adjustment factor k , according to the power line parameters:
；
where s is the length of the power line and b is the total width of the power line;
calculating a second scaling factor k 1 and a second adjustment factor ：
;
Wherein h is the thickness of the power line;
Calculating a first scaling factor k and a first adjustment factor k ,, a second scaling factor k 1 and a second adjustment factor Is the first type of elliptic integral of (a):
；
Where x is input data and includes a first scaling factor k and a first adjustment factor k , and a second scaling factor k 1 and a second adjustment factor ，As an integral variable, the regional range is 0 to pi/2;
Calculating effective dielectric constant of power line according to first-class elliptic integral ：
；
Wherein the method comprises the steps ofThe relative dielectric constant of the material of the power circuit;
According to the effective dielectric constant Calculating the characteristic impedance Z 0 of the power line:
；
Under the no-load state of the electric power energy system, using TDR equipment to inject a high-frequency pulse signal into an electric power line as an incident voltage signal, collecting a reflected voltage signal, taking the ratio of the reflected voltage signal to the incident voltage signal as a no-load reflection coefficient, and calculating the no-load impedance of the electric power energy system based on the no-load reflection coefficient:
；
where Z r is the no-load impedance, Is the no-load reflection coefficient;
Repeatedly operating in a state that the electric power energy system is connected to a load to obtain the load impedance Z y of the electric power energy system, and calculating an impedance change coefficient S according to the load impedance and the no-load impedance:
；
Selecting power lines at different positions in an electric power energy system, calculating to obtain impedance change coefficients by using TDR equipment, recording the position tested each time and the time difference t between an incident voltage signal and a reflected voltage signal, forming a line graph according to the impedance change coefficients at all positions from small to large, identifying peaks in the line graph, extracting the test position of the impedance change coefficient at the peak position, calculating the distance between a load node and the test position according to the time difference t and the signal speed, and further determining the position of the load node.
The method comprises the steps of arranging a sensor in an electric power energy system, collecting electric power data and electric power line parameters in real time, ensuring the real-time performance and accuracy of the data, providing a reliable basis for subsequent calculation, calculating a first scale factor (k and k ') and a second scale factor (k 1 and k 1'), carrying out first elliptic integral calculation, accurately determining the effective dielectric constant of the electric power line, greatly improving the calculation accuracy of the electric characteristics of the electric power line, injecting high-frequency pulse signals by using TDR equipment and measuring reflected signals in a non-load state and a load state, accurately acquiring non-load impedance and load impedance of the electric power line, thereby calculating impedance change coefficients, accurately identifying discontinuous points and load nodes in the electric power line by analyzing the reflected signals, providing data support for subsequent load position determination, reflecting the impedance change condition of the electric power line in different load states, intuitively identifying the position of the load nodes by measuring at different positions, forming a line graph of the impedance change coefficients, accurately identifying the peak position of the load nodes in the line, accurately obtaining the non-load position corresponding to the load node position, accurately obtaining the non-load impedance and the load impedance of the electric power line by using TDR equipment and measuring the reflected signals, accurately obtaining the non-load impedance and load impedance of the electric power line, accurately analyzing the load impedance and the load node, and accurately determining the load position of the load node, and the load position of the electric power line, and the load change coefficient under the condition, and the load position of the load system, and the load change coefficient can be accurately and load-carrying and load position.
It should be noted that the sensors include a current sensor, a voltage sensor, an energy consumption sensor, and a GPS sensor, and the current sensor, the voltage sensor, the energy consumption sensor, and the GPS sensor are each allocated to a group, and are disposed in an equidistant manner in the electric power energy system.
The sensor groups are deployed at equal intervals in the power energy system, the comprehensiveness and uniformity of data acquisition are ensured, blind areas and deviations of data acquisition are avoided, the equidistant deployment enables the system to uniformly cover the whole monitoring area, the representativeness of the data is improved, the current sensor and the voltage sensor collect current and voltage data in a power line in real time, high-precision instantaneous power parameters are provided, the data can be used for calculating instantaneous power, load conditions and power quality, the energy consumption data provided by the energy consumption sensor comprise active power, reactive power and total energy consumption, the energy utilization efficiency of the system can be comprehensively evaluated, energy consumption analysis and energy distribution can be carried out through the data, energy waste is reduced, the geographic position information provided by the GPS sensor is ensured, the accurate deployment of the sensor groups and the geographic marking of the data are ensured, the geographic information is helpful for positioning fault points, the sensor layout is optimized, and the monitoring precision and the response speed of the system are improved.
S2, monitoring load nodes by using a non-invasive load monitoring technology and extracting load characteristics;
Specifically, using a non-invasive load monitoring technology to monitor load nodes and extract load characteristics refers to collecting power data of the load nodes in real time through a sensor after determining the positions of the load nodes to obtain current I k and voltage V k sampled in real time, and constructing a recursive model R:
；
Where L is the total number of current samples, I k (I) and I k (j) are the ith and kth currents, respectively, As a recursive distance threshold value,The function value is 1 when x is more than or equal to 0, otherwise, the function value is 0;
To make the features more pronounced, a thresholdless recursive model T is used:
；
Introduction of a scaling factor Performing exponential scaling on the thresholdless recursive model T to obtain a scaled recursive model S:
；
Finally, the self-adaptive scaling recursive model is obtained by combining the self-adaptive scaling coefficient with the phase difference information between the current and the voltage;
The pearson correlation coefficient of the current I k and the voltage V k is calculated and the scaling factor is calculated:
；
Wherein the method comprises the steps of For scaling factor, cor (I k,Vk) is the pearson correlation of current I k and voltage V k;
constructing an adaptive scaling recursion model A according to the scaling coefficient:
；
Wherein I k (I) and I k (j) are the I-th and k-th currents, respectively, L is the total number of current samples;
And obtaining an adaptive scaling recursion diagram through the adaptive scaling recursion model A, identifying the adaptive scaling recursion diagram by using a depth vision processing network Swin-transform, and extracting load characteristics of load nodes in the adaptive scaling recursion diagram, wherein the load characteristics comprise average power consumption, average running time and daily running times.
The current and voltage data of the load node are collected in real time through the sensor, timeliness and accuracy of the data are guaranteed, the data are the basis of subsequent feature extraction and analysis, the real-time data collection is the core of the monitoring system, the instant running state of the load node can be reflected, a reliable basis is provided for system optimization, and the recursion diagram reveals the dynamic characteristics of the system by showing the recursion behaviors of the current and voltage data points in the phase space. The method comprises the steps that a recursive graph enables characteristics of load nodes to be clearer, threshold values are eliminated by the aid of the recursive graph without threshold values, the characteristics are more obvious, characteristics of data are further emphasized by scaling the recursive graph through exponential scaling, accuracy of characteristic extraction is improved, characteristics of electric power data are dynamically reflected through self-adaptive adjustment of scaling coefficients and combination of phase difference information between current and voltage, accuracy of load characteristic extraction is improved by the aid of the self-adaptive scaling recursive graph, linear correlation between the current and the voltage is measured by pearson correlation coefficients, scaling coefficients are used for adjusting scaling of the recursive graph, characteristic extraction of the recursive graph is enabled to be more targeted, accuracy and robustness of characteristic extraction can be further improved through calculation of the correlation coefficients and the scaling coefficients, the load characteristics in the self-adaptive recursive graph are effectively extracted by means of a multi-level window self-attention mechanism of the Swin-Transformer, and the characteristics reflect running states and energy consumption modes of the load nodes.
S3, a machine learning algorithm is constructed according to the load characteristics, the state of the electric power energy system is analyzed for early warning, and the electric power energy system is optimized and adjusted based on the multi-layer perceptron neural network;
Specifically, a machine learning algorithm is constructed according to the load characteristics, the state of the electric power energy system is analyzed for early warning, a convolutional neural network is constructed, and an input format is defined as a load characteristic format;
Collecting historical power data in a power energy system, extracting historical load characteristics, inputting the historical load characteristics as a training set into a convolutional neural network for training, and optimizing parameters of the convolutional neural network;
Inputting the load characteristics extracted through the real-time data of the load nodes into an optimized convolutional neural network, and outputting the state of an electric power energy system, including normal and abnormal states;
if the state of the electric power energy system is normal, monitoring is kept;
and if the state of the electric power energy system is abnormal, early warning is carried out.
The convolutional neural network extracts local characteristics of data through convolutional operation, and extracts higher-level characteristics gradually through a multi-layer network structure, processing capacity of complex data is improved, an input format is defined as a load characteristic format, the model can accurately receive and process electric power data, historical load characteristics are extracted through collecting and analyzing historical electric power data, a rich training data set is formed, the data reflect operation characteristics of an electric power system in different states, a sufficient sample is provided for model training, the historical load characteristics are input into the convolutional neural network for training, network parameters are optimized continuously, the model can accurately identify the operation state of the electric power system, the training process comprises forward propagation and backward propagation, the prediction precision of the model is improved by minimizing loss function adjustment parameters, the load characteristics extracted by load node real-time data are input into the convolutional neural network after optimization, the state of the system is analyzed in real time, the model outputs electric power system state according to the real-time data, the electric power system state comprises normal and abnormal states, the real-time response of the system is ensured, if the electric power system state is normal, monitoring is kept, the system is ensured to operate in a normal range, if the system state is abnormal, early warning is immediately carried out, maintenance staff is prompted, and necessary fault loss is avoided.
Further, optimizing and adjusting the electric power energy system based on the multi-layer perceptron neural network means constructing the multi-layer perceptron neural network,
Collecting historical load characteristics of all load nodes in the electric power energy network, training the multi-layer perceptron neural network, optimizing the target to minimize the operation energy consumption, and optimizing the parameters of the multi-layer perceptron neural network based on forward propagation, backward propagation and minimized loss function;
and collecting load characteristics of all load nodes in the electric power energy network, inputting the load characteristics into the trained multi-layer perceptron neural network, outputting the optimal running time and the optimal running load of each load node, and adjusting the electric power energy system based on the optimal running time and the optimal running load.
The method comprises the steps of constructing an MLP neural network, ensuring that the network can process complex nonlinear relations by setting an input layer, a hidden layer and an output layer, enabling the input layer to receive load characteristic data, enabling the hidden layer to extract deep characteristics of data through an activation function, enabling the output layer to generate optimal operation time and optimal operation load, forming a training data set by collecting historical load characteristics of all load nodes in an electric power energy network, enabling the data to reflect the load characteristics of the system in different operation states, providing rich samples for model training, training the MLP neural network based on forward propagation, reverse propagation and an optimization method of a minimum loss function, calculating output results through forward propagation, calculating gradients through reverse propagation, updating network parameters, enabling loss function values to be gradually reduced, enabling the real-time load characteristics of all load nodes in the electric power energy network to be input into the trained MLP neural network, and enabling the network to output the optimal operation time and the optimal operation load of all load nodes according to the real-time data.
S4, constructing a user interaction interface to display the power data in real time;
specifically, building the user interaction interface to display the power data in real time means building the user interface to display the power data collected by the sensor, adding an interaction function for the user interface, allowing a user to customize the power data display content, and synchronously displaying the high-brightness identification of the load node in the power energy system in the user interface.
By constructing the user interaction interface, an intuitive graphical display mode is provided, a user can conveniently view and understand electric data, the interaction interface supports various display modes including charts, curves, maps and the like, the diversity and flexibility of data display are improved, real-time display of the electric data is realized, the user can see the latest data change and system state in the first time, the method has important significance for timely finding and processing abnormal conditions, optimizing the system operation, adding interaction functions to the user interface, allowing the user to interact with the interface through clicking, dragging, zooming and other operations, the user can customize display contents according to actual requirements, different data views and display modes are selected, and the flexibility and usability of the interface are improved.
Examples
Referring to fig. 2, for a second embodiment of the present invention, which is different from the previous embodiment, there is provided a monitoring system based on an electric power energy management system, which includes,
A data collection module for deploying sensors in the power energy system to collect power data;
The load identification module is used for identifying load nodes according to the power data and calculating the positions of the load nodes;
the characteristic extraction module is used for extracting load characteristics according to the power data of the load nodes;
The early warning optimization module is used for analyzing and early warning the state of the electric power energy system according to the load characteristics and synchronously optimizing and adjusting the electric power energy system;
And the display module is used for displaying the power data and the load nodes to a user.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.

Claims (6)

1.一种基于电力能源管理系统的监控方法，其特征在于：包括，1. A monitoring method based on an electric power energy management system, characterized in that: it includes: 收集电力数据，通过时域反射计确定电力能源系统中负载节点位置；Collect power data and determine the location of load nodes in the power energy system through time domain reflectometry; 使用非侵入式负载监控技术监测负载节点并提取负荷特征；Use non-intrusive load monitoring technology to monitor load nodes and extract load characteristics; 根据负荷特征构建机器学习算法分析电力能源系统状态进行预警，并基于多层感知机神经网络优化调整电力能源系统；Build a machine learning algorithm based on load characteristics to analyze the status of the power energy system for early warning, and optimize and adjust the power energy system based on a multi-layer perceptron neural network; 构建用户交互界面实时展示电力数据；Build a user interactive interface to display power data in real time; 所述收集电力数据，通过时域反射计确定电力能源系统中负载节点位置指在电力能源系统中部署传感器收集电力数据，并收集电力能源系统中电力线路参数，根据电力线路参数计算第一比例因子k和第一调整因子：The collecting of power data and determining the location of the load node in the power energy system by time domain reflectometry refers to deploying sensors in the power energy system to collect power data and collect power line parameters in the power energy system, and calculating the first proportionality factor k and the first adjustment factor according to the power line parameters. : ； ; 其中s为电力线路长度，b为电力线路总宽度；Where s is the length of the power line, b is the total width of the power line; 计算第二比例因子k1和第二调整因子；Calculate the second scaling factor k1 and the second adjustment factor ; ； ; 其中h为电力线路的厚度；Where h is the thickness of the power line; 计算第一比例因子k和第一调整因子k，、第二比例因子k1和第二调整因子的第一类椭圆积分：Calculate the first scale factor k and the first adjustment factor k , the second scale factor k1 and the second adjustment factor Elliptic integral of the first kind: ； ; 其中x为输入数据，包括第一比例因子k和第一调整因子k，以及第二比例因子k1和第二调整因子，为积分变量，区域范围为0到π/2；Where x is the input data, including the first scale factor k and the first adjustment factor k , and the second scale factor k1 and the second adjustment factor , is the integral variable, and its region ranges from 0 to π/2; 根据第一类椭圆积分计算电力线路的有效介电常数：Calculation of effective dielectric constant of power lines based on elliptic integral of the first kind : ； ; 根据有效介电常数计算电力线路的特性阻抗Z0：According to the effective dielectric constant Calculate the characteristic impedance Z 0 of the power line: ； ; 在电力能源系统无负载状态下使用TDR设备向电力线路注入高频脉冲信号作为入射电压信号，并收集反射电压信号，将反射电压信号和入射电压信号的比值作为无负载反射系数，基于无负载反射系数计算电力能源系统的无负载阻抗：In the no-load state of the electric energy system, a TDR device is used to inject a high-frequency pulse signal into the power line as the incident voltage signal, and the reflected voltage signal is collected. The ratio of the reflected voltage signal to the incident voltage signal is used as the no-load reflection coefficient. The no-load impedance of the electric energy system is calculated based on the no-load reflection coefficient: ； ; 其中Zr为无负载阻抗，为无负载反射系数；Where Zr is the no-load impedance, is the no-load reflection coefficient; 在电力能源系统接入负载状态下重复操作获取电力能源系统的负载阻抗Zy，并根据负载阻抗和无负载阻抗计算阻抗变化系数S：Repeat the operation when the electric energy system is connected to a load to obtain the load impedance Z y of the electric energy system, and calculate the impedance variation coefficient S according to the load impedance and the no-load impedance: ； ; 在电力能源系统中选择不同位置的电力线路使用TDR设备计算获得阻抗变化系数，记录每次测试的位置和入射电压信号与反射电压信号时间差t，按照距离从小到大将所有位置的阻抗变化系数形成折线图，识别折线图中峰值，提取峰值位置的阻抗变化系数的测试位置，并根据时间差t和信号速度计算负载节点与测试位置的距离，进一步确定负载节点位置；Select power lines at different locations in the power energy system and use TDR equipment to calculate the impedance variation coefficient, record the location of each test and the time difference t between the incident voltage signal and the reflected voltage signal, form a line graph of the impedance variation coefficients of all locations from small to large distances, identify the peak in the line graph, extract the test location of the impedance variation coefficient at the peak location, and calculate the distance between the load node and the test location based on the time difference t and the signal speed, and further determine the load node location; 所述使用非侵入式负载监控技术监测负载节点并提取负荷特征指确定负载节点位置后通过传感器实时收集负载节点的电力数据得到实时采样的电流Ik和电压Vk，计算电流Ik和电压Vk的皮尔逊相关系数并计算缩放系数：The use of non-intrusive load monitoring technology to monitor load nodes and extract load characteristics refers to determining the location of the load node, collecting the power data of the load node in real time through sensors to obtain real-time sampled current Ik and voltage Vk , calculating the Pearson correlation coefficient of the current Ik and the voltage Vk, and calculating the scaling factor: ； ; 其中为缩放系数，cor（Ik，Vk）为电流Ik和电压Vk的皮尔逊相关系数；in is the scaling factor, cor(I k , V k ) is the Pearson correlation coefficient of current I k and voltage V k ; 根据缩放系数构建自适应缩放递归模型A：Construct an adaptive scaling recursive model A according to the scaling factor: ； ; 其中Ik（i）和Ik（j）分别为第i个和第k个电流，L为电流采样总数；Where I k (i) and I k (j) are the i-th and k-th currents respectively, and L is the total number of current samples; 通过自适应缩放递归模型A得到自适应缩放递归图，使用深度视觉处理网络Swin-Transformer对自适应缩放递归图进行识别，提取其中的负载节点的负荷特征包括平均功耗、平均运行时间以及每日运行次数；The adaptive scaling recursive graph is obtained through the adaptive scaling recursive model A, and the deep visual processing network Swin-Transformer is used to identify the adaptive scaling recursive graph, and the load characteristics of the load nodes therein are extracted, including the average power consumption, average running time and the number of daily operations; 所述根据负荷特征构建机器学习算法分析电力能源系统状态进行预警指构建卷积神经网络，定义输入格式为负荷特征格式；The constructing of a machine learning algorithm according to load characteristics to analyze the state of the electric energy system for early warning refers to constructing a convolutional neural network and defining the input format as a load characteristic format; 采集电力能源系统中历史电力数据提取历史负荷特征，将历史负荷特征作为训练集输入卷积神经网络中进行训练，优化卷积神经网络参数；Collect historical power data in the power energy system to extract historical load characteristics, input the historical load characteristics into the convolutional neural network as a training set for training, and optimize the convolutional neural network parameters; 将通过负载节点实时数据提取的负荷特征输入优化的卷积神经网络中，输出电力能源系统状态，包括正常和异常；The load characteristics extracted from the real-time data of the load nodes are input into the optimized convolutional neural network to output the power energy system status, including normal and abnormal; 若电力能源系统状态为正常，则保持监测；If the power energy system status is normal, then keep monitoring; 若电力能源系统状态为异常，则进行预警；If the power energy system status is abnormal, an early warning will be issued; 所述基于多层感知机神经网络优化调整电力能源系统指构建多层感知机神经网络，The optimization and adjustment of the electric energy system based on the multi-layer perceptron neural network refers to constructing a multi-layer perceptron neural network, 收集电力能源网络中所有负载节点的历史负荷特征对多层感知机神经网络进行训练，优化目标为最小化运行能耗，基于前向传播、反向传播以及最小化损失函数优化多层感知机神经网络参数；Collect historical load characteristics of all load nodes in the power energy network to train the multi-layer perceptron neural network. The optimization goal is to minimize the operating energy consumption. The parameters of the multi-layer perceptron neural network are optimized based on forward propagation, back propagation and minimization of loss function. 收集电力能源网络中所有负载节点的负荷特征输入训练好的多层感知机神经网络，输出各负载节点的最优运行时间和最优运行负载，基于最优运行时间和最优运行负载对电力能源系统进行调整。The load characteristics of all load nodes in the power energy network are collected and input into the trained multi-layer perceptron neural network, the optimal operating time and optimal operating load of each load node are output, and the power energy system is adjusted based on the optimal operating time and optimal operating load. 2.如权利要求1所述的基于电力能源管理系统的监控方法，其特征在于：所述传感器包括电流传感器、电压传感器、能耗传感器以及GPS传感器，将电流传感器、电压传感器、能耗传感器以及GPS传感器各一个分配成组，在电力能源系统中进行等距部署。2. The monitoring method based on the electric power energy management system as described in claim 1 is characterized in that: the sensors include current sensors, voltage sensors, energy consumption sensors and GPS sensors, and the current sensors, voltage sensors, energy consumption sensors and GPS sensors are each assigned into a group and deployed equidistantly in the electric power energy system. 3.如权利要求2所述的基于电力能源管理系统的监控方法，其特征在于：所述构建用户交互界面实时展示电力数据指构建用户界面将传感器收集的电力数据进行展示，为用户界面添加交互功能，允许用户自定义电力数据展示内容，将负载节点在电力能源系统中高亮标识同步展示在用户界面中。3. The monitoring method based on the electric power energy management system as described in claim 2 is characterized in that: the construction of a user interaction interface to display power data in real time refers to constructing a user interface to display the power data collected by the sensor, adding interactive functions to the user interface, allowing users to customize the power data display content, and highlighting the load nodes in the electric power system and synchronously displaying them in the user interface. 4.一种如权利要求1-3任一所述的基于电力能源管理系统的监控方法的基于电力能源管理系统的监控系统，其特征在于：包括，4. A monitoring system based on an electric energy management system and a monitoring method based on an electric energy management system as claimed in any one of claims 1 to 3, characterized in that: it comprises: 数据收集模块，用于在电力能源系统中部署传感器收集电力数据；A data collection module, used to deploy sensors in the electric energy system to collect electric power data; 负载识别模块，用于根据电力数据识别负载节点并计算负载节点位置；A load identification module, used to identify load nodes and calculate load node locations based on power data; 特征提取模块，用于根据负载节点的电力数据提取负荷特征；A feature extraction module, used for extracting load features according to power data of load nodes; 预警优化模块，用于根据负荷特征对电力能源系统状态进行分析预警，同步对电力能源系统进行优化调整；The early warning optimization module is used to analyze and warn the status of the power energy system according to the load characteristics, and simultaneously optimize and adjust the power energy system; 展示模块，用于将电力数据和负载节点向用户进行展示。The display module is used to display power data and load nodes to users. 5.一种计算机设备，包括：存储器和处理器；所述存储器存储有计算机程序，其特征在于：所述处理器执行所述计算机程序时实现权利要求1至3中任一项所述的基于电力能源管理系统的监控方法的步骤。5. A computer device, comprising: a memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the monitoring method based on the electric power energy management system described in any one of claims 1 to 3 are implemented. 6.一种计算机可读存储介质，其上存储有计算机程序，其特征在于：所述计算机程序被处理器执行时实现权利要求1至3中任一项所述的基于电力能源管理系统的监控方法的步骤。6. A computer-readable storage medium having a computer program stored thereon, characterized in that: when the computer program is executed by a processor, the steps of the monitoring method based on the electric power energy management system described in any one of claims 1 to 3 are implemented.
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