CN117994081A

CN117994081A - Distributed multi-energy system state sensing method based on dual dynamic threshold

Info

Publication number: CN117994081A
Application number: CN202410275249.7A
Authority: CN
Inventors: 马大中; 宋婉毓; 蒋屹新; 韩议莹; 张宇南; 李炜; 徐文倩; 张佳敏; 梁志宏; 张峻凯; 许爱东; 杨祎巍
Original assignee: CSG Electric Power Research Institute
Current assignee: CSG Electric Power Research Institute
Priority date: 2024-03-11
Filing date: 2024-03-11
Publication date: 2024-05-07

Abstract

The application provides a distributed multi-energy system state sensing method based on a dual dynamic threshold value, which comprises the following steps: determining a historical covariance matrix and a real-time covariance matrix, determining a historical matrix spectrum difference value according to the historical covariance matrix, thereby determining a first dynamic threshold value, determining a real-time matrix spectrum difference value according to the real-time covariance matrix, and determining a first system state of the multi-energy system according to the magnitude relation between the real-time matrix spectrum difference value and the first dynamic threshold value to obtain a first judging result; determining a second system state of the multi-energy system according to the magnitude relation between the maximum eigenvalue of the estimated covariance and the second dynamic threshold value, and obtaining a second judgment result; and under the condition that the first judging result and the second judging result are the same, determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system. The method considers different time scales of the multi-energy system and increases the accuracy of state judgment of the multi-energy system.

Description

Distributed multi-energy system state sensing method based on dual dynamic threshold

Technical Field

The application relates to the field of state sensing of a distributed multi-energy system, in particular to a state sensing method of the distributed multi-energy system based on a dual dynamic threshold, a state sensing device of the distributed multi-energy system based on the dual dynamic threshold, a computer readable storage medium and electronic equipment.

Background

In recent years, with the continuous development of the big data age, smart grids are continuously advancing, and the data volume is continuously increasing in an exponential level. Meanwhile, the construction and development of the multi-energy system also rapidly progress, and the method is significant for the safe and stable operation of the multi-energy system by providing an effective research means for the construction and development of the multi-energy system through a big data algorithm in the face of complex data components and time scales of the multi-energy system.

Matrix spectral distribution theory is a theoretical method with good applicability, and in research and development, the theory is gradually extended into a plurality of discipline fields. The spectrum distribution theory does not need to build a physical model for the system, so that the difficulty in sensing the state of the multi-energy system is reduced to a great extent. At present, in the aspect of a state sensing method, an average spectrum radius is used as a characteristic statistic for matrix spectrum distribution, and the real-time state of a system is monitored according to a single-loop law to judge whether an event occurs. There are also schemes for studying the influence of system factor variables on the system by taking eigenvalues of covariance matrix as observation features. However, the above researches are mostly directed to a single energy system, and are not applicable to the sensing of the operation state of a multi-energy system without considering the time scale problem, so the following disadvantages exist in the prior art:

1. In the current state sensing method based on matrix spectrum distribution, most of the state sensing method only considers a power system, and does not consider a large-scale multi-energy system with more complex equipment types, so that the application range of the state sensing method has certain limitation;

2. The existing algorithm is basically a constant threshold value used when judging the system state, has low precision and is easy to misjudge the state;

3. In the existing algorithm, the time scale problem is rarely considered, so that the system detection matrix is inaccurate, the detection frequency is high, and the situation that the event occurs but the system is judged to be normal is easy to generate although the calculation is simple;

4. In the aspect of perception of the system state, the state of the system is mostly judged on the basis of the establishment of a mechanism model, but as the components of the multi-energy system are more and more complex, the system is difficult to accurately model.

Disclosure of Invention

The application mainly aims to provide a distributed multi-energy system state sensing method based on a dual dynamic threshold, a distributed multi-energy system state sensing device based on the dual dynamic threshold, a computer readable storage medium and electronic equipment, so as to at least solve the problem that the existing system state determining method is not considered in time scale, can only be applied to a single energy system and cannot be applied to a multi-energy system.

To achieve the above object, according to one aspect of the present application, there is provided a distributed multi-energy system state sensing method based on dual dynamic thresholds, including: step S1: determining a historical data original matrix according to historical operation data of a multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale; step S2: determining a spectrum difference value calculation formula based on a linear function and a divergence theorem, determining a spectrum difference value of the historical data spectrum distribution function and a theoretical spectrum distribution function as a historical matrix spectrum difference value according to the spectrum difference value calculation formula, and determining a first dynamic threshold gamma ₁ according to the historical matrix spectrum difference value, wherein the first dynamic threshold is used for detecting the system state of the multi-energy system; step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference valueWherein the real-time covariance matrix is related to a time scale; step S4: according to the real-time matrix spectrum difference value/>Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁ to obtain a first judgment result; step S5: calculating the signal-to-noise ratio of the multi-energy system at the current moment, determining a second dynamic threshold gamma ₂ according to the signal-to-noise ratio, and calculating the real-time covariance matrix by adopting a Rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max; step S6: determining a second system state of the multi-energy system according to the magnitude relation between the maximum eigenvalue lambda _max of the estimated covariance and the second dynamic threshold gamma ₂, and obtaining a second judgment result; step S7: and under the condition that the first judging result and the second judging result are the same, determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system.

Optionally, the multi-energy system includes an electric power sub-network, a natural gas sub-network, and a thermal sub-network, and the step S1 includes: acquiring historical operation data of the multi-energy system in a historical time period and three time scales, wherein the three time scales are respectively a short time scale, a medium time scale and a long time scale; constructing three different time scale matrices X _H∈C_n×T based on the three time scales, the corresponding detection nodes of the multi-energy system and the corresponding sampling time, wherein X _H＝[x_t-T+1,x_t-T+2,…,x_t and T are the sampling time lengths,N _e is the number of detection nodes of the electric power sub-network, n _g is the number of detection nodes of the natural gas sub-network, and n _h is the number of detection nodes of the thermal sub-network; respectively carrying out normalization processing on the three time scale matrixes to obtain a normalization processing matrix/>Carrying out standardization processing on the row vectors of the normalization processing matrix to obtain a standardization processing matrix/>And carrying out target transformation on the normalization processing matrix and the normalization processing matrix to obtain the historical covariance matrix and the historical data spectrum distribution function.

Optionally, the multi-energy system includes an electric power sub-network, a natural gas sub-network, and a thermal sub-network, the multi-energy system includes three time scales, namely a short time scale, a medium time scale, and a long time scale, and the step S2 includes: determining a spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T) based on a linear function and a divergence theorem, wherein eta is a constant, JS (·) is a JS divergence variation value, P _t ⁱ is a matrix spectrum distribution function at a time t, i=e, g, h, e is a covariance matrix spectrum distribution function corresponding to a short-time scale detection matrix, g is a covariance matrix spectrum distribution function corresponding to a medium-time scale detection matrix, h is a covariance matrix spectrum distribution function corresponding to a long-time scale detection matrix, and P ^T is a random matrix theoretical spectrum distribution; according to the historical data spectrum distribution function and the spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T)), determining the historical matrix spectrum difference values respectively corresponding to the three time scalesWherein/>Determining the first dynamic threshold gamma ₁ according to the historical matrix spectrum difference values and dynamic errors respectively corresponding to the three time scales, wherein/>

Optionally, the step S4 includes: spectral difference values in said real-time matrixIf the first dynamic threshold value gamma ₁ is larger than the first dynamic threshold value gamma ₁, determining that the first system state is an abnormal state; at the real-time matrix spectral difference value/>And under the condition of being smaller than or equal to the first dynamic threshold gamma ₁, determining that the first system state is a non-abnormal state.

Optionally, calculating a signal-to-noise ratio of the multi-energy system at a current moment, and determining a second dynamic threshold γ ₂ according to the signal-to-noise ratio, including: determining a frequency domain signal x _i(e^jw according to the real-time covariance matrix); according to the bandwidth formula of the spectral densityDetermining a spectral density bandwidth, wherein b is the spectral density bandwidth, I ₀ [. Cndot. ] is a Bessel function, beta is a constant value, [. Cndot. ] _RMS is a root mean square value, and μ (. Cndot.) is a variance; according to the frequency domain signal, the spectral density bandwidth and a signal power formula/>And noise power formulaDetermining a signal power P _s and a noise power P _n; determining the signal-to-noise ratio SNR from the signal power and the noise power, wherein/>Determining the second dynamic threshold gamma ₂ from the signal-to-noise ratio, wherein/>

Optionally, the real-time covariance matrix is calculated by adopting a rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max, which comprises: construction of iterative algorithm formulaWherein v _k is a feature vector corresponding to the maximum feature value of the real-time covariance matrix, alpha _k is an iteration vector, and the initial value is alpha ₀＝{1,1,…,1}_n×1; according to the iterative algorithm formula, an estimated covariance maximum eigenvalue lambda _max is determined, wherein lambda _max≈max(v_k).

Optionally, the step S6 includes: determining that the second system state is an abnormal state if the estimated covariance maximum eigenvalue λ _max is greater than the second dynamic threshold value γ ₂; and determining that the second system state is a non-abnormal state in the case that the estimated covariance maximum eigenvalue lambda _max is less than or equal to the second dynamic threshold gamma ₂.

According to another aspect of the present application, there is provided a distributed multi-energy system state sensing device based on dual dynamic thresholds, including: a first processing unit, configured to execute step S1: determining a historical data original matrix according to historical operation data of a multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale; a second processing unit, configured to execute step S2: determining a spectrum difference value calculation formula based on a linear function and a divergence theorem, determining a spectrum difference value of the historical data spectrum distribution function and a theoretical spectrum distribution function as a historical matrix spectrum difference value according to the spectrum difference value calculation formula, and determining a first dynamic threshold gamma ₁ according to the historical matrix spectrum difference value, wherein the first dynamic threshold is used for detecting the system state of the multi-energy system; a third processing unit, configured to execute step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference valueWherein the real-time covariance matrix is related to a time scale; a fourth processing unit for executing step S4: according to the real-time matrix spectrum difference value/>Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁ to obtain a first judgment result; a fifth processing unit, configured to execute step S5: calculating the signal-to-noise ratio of the multi-energy system at the current moment, determining a second dynamic threshold gamma ₂ according to the signal-to-noise ratio, and calculating the real-time covariance matrix by adopting a Rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max; a sixth processing unit, configured to execute step S6: determining a second system state of the multi-energy system according to the magnitude relation between the maximum eigenvalue lambda _max of the estimated covariance and the second dynamic threshold gamma ₂, and obtaining a second judgment result; a seventh processing unit, configured to perform step S7: and under the condition that the first judging result and the second judging result are the same, determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system.

According to another aspect of the present application, there is provided a computer readable storage medium, where the computer readable storage medium includes a stored program, and when the program runs, the device where the computer readable storage medium is controlled to execute any one of the distributed multi-energy system state sensing methods based on dual dynamic thresholds.

According to another aspect of the present application, there is provided an electronic apparatus including: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the dual dynamic threshold based distributed multi-energy system state awareness methods.

By applying the technical scheme of the application, the distributed multi-energy system state sensing method based on the dual dynamic threshold value uses a matrix spectrum distribution theory to analyze a detection matrix formed by system operation data, forms a dynamic threshold value by using the spectrum difference value of historical data, and obtains the spectrum difference value of the detection matrix to judge. And calculating the signal-to-noise ratio at the current moment, calculating the double dynamic threshold by using the signal-to-noise ratio, and judging by taking the covariance matrix eigenvalue of the large-dimension matrix as the eigenvalue. The detection matrix is divided into short, medium and long time scales so as to increase the accuracy of state judgment, and the problem that the existing system state determining method is only applicable to a single energy system and cannot be applied to a multi-energy system without considering the time scale is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

FIG. 1 illustrates a hardware block diagram of a mobile terminal performing a distributed multi-energy system state awareness method based on dual dynamic thresholds, provided in an embodiment in accordance with the application;

FIG. 2 is a flow chart of a distributed multi-energy system state sensing method based on dual dynamic thresholds according to an embodiment of the present application;

FIG. 3 illustrates a network diagram of system nodes used by an embodiment of a distributed multi-energy system state awareness method based on dual dynamic thresholds provided in accordance with an embodiment of the present application;

FIG. 4 is a flow chart of another distributed multi-energy system state sensing method based on dual dynamic thresholds according to an embodiment of the present application;

fig. 5 shows a block diagram of a distributed multi-energy system state sensing device based on dual dynamic thresholds according to an embodiment of the present application.

Wherein the above figures include the following reference numerals:

102. a processor; 104. a memory; 106. a transmission device; 108. and an input/output device.

Detailed Description

It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.

In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

As described in the background art, in the existing algorithm, the problem of time scale is rarely considered, which causes inaccurate system detection matrix and high detection frequency, so that the situation that events occur but the detection is normal is easy to generate though the calculation is simple; in order to solve the problem that the existing system state determining method is not considered in time scale and can only be applied to a single-energy system but not to a multi-energy system, the embodiment of the application provides a distributed multi-energy system state sensing method based on a dual dynamic threshold, a distributed multi-energy system state sensing device based on the dual dynamic threshold, a computer readable storage medium and electronic equipment.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the operation on a mobile terminal as an example, fig. 1 is a hardware structural block diagram of a mobile terminal of a distributed multi-energy system state sensing method based on dual dynamic thresholds according to an embodiment of the present application. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.

The memory 104 may be used to store computer programs, such as software programs and modules of application software, such as a computer program corresponding to a distributed multi-energy system state sensing method based on dual dynamic thresholds in the embodiment of the present invention, and the processor 102 executes the computer programs stored in the memory 104 to perform various functional applications and data processing, that is, implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.

In this embodiment, a dual dynamic threshold based distributed multi-energy system state sensing method operating on a mobile terminal, a computer terminal, or a similar computing device is provided, it should be noted that the steps illustrated in the flowchart of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that illustrated herein.

FIG. 2 is a flow chart of a distributed multi-energy system state awareness method based on dual dynamic thresholds, according to an embodiment of the present application. As shown in fig. 2, the method comprises the steps of:

step S1: determining a historical data original matrix according to historical operation data of the multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale;

in particular, since three different energy sources of electricity-gas-heat are contained in the multi-energy system, there are different time scales in the operation process, three different time scale matrixes are constructed according to the different time scales, wherein in the short time scale, the matrix is composed of node voltages of the electric power sub-network, in the medium time scale, the matrix is composed of node voltages of the electric power sub-network and node pressures of the natural gas sub-network, and in the long time scale, the matrix is composed of node voltages of the electric power sub-network, node pressures of the natural gas sub-network and node pressures of the thermal sub-network.

The network structure of the multi-energy system in this embodiment is shown in fig. 3, and uses a 33-node power system+20-node natural gas system+32-node thermodynamic system, which is formed by coupling and improving a standard power system (IEEE 33-node power system) and a standard natural gas system (20-node natural gas system) and a standard thermodynamic system (32-node thermodynamic system). The power system comprises a large CHP unit, a micro CHP unit, three thermal power units and three photovoltaic power units; the natural gas system comprises 21 gas pipelines, 2 pressurizing stations and 2 gas source points; the thermodynamic system comprises two gas boilers and a heat pump. The specifically set parameters according to the change are shown in table 1.

Table 1 sets the load variation parameters

Wherein, the multi-energy system comprises an electric power sub-network, a natural gas sub-network and a thermal sub-network, and the step S1 comprises the following steps:

Step S11, acquiring historical operation data of the multi-energy system and three time scales in a historical time period, wherein the three time scales are respectively a short time scale, a medium time scale and a long time scale;

Step S12, constructing three different time scale matrixes X _H∈C_n×T based on the three time scales, the corresponding detection nodes of the multi-energy system and the corresponding sampling time, wherein X _H＝[x_t-T+1,x_t-T+2,…,x_t and T are the sampling time length, N _e is the number of detection nodes of the electric power sub-network, n _g is the number of detection nodes of the natural gas sub-network, and n _h is the number of detection nodes of the thermal sub-network; in this embodiment, the sampling time t=100s and n _e＝33,n_g＝20,n_h =32 are set

Step S13, respectively carrying out normalization processing on the three time scale matrixes to obtain a normalization processing matrixWherein/>

Step S14, carrying out standardization processing on the row vectors of the normalization processing matrix to obtain a standardization processing matrixWherein, the row vector of the normalization processing matrix is expressed as/>Carrying out standardization treatment;

And S15, performing target transformation on the normalization processing matrix and the normalization processing matrix to obtain the historical covariance matrix and the historical data spectrum distribution function.

Wherein, according to the formulaPerforming target transformation on the normalized processing matrix and the normalized processing matrix to obtain the history covariance matrix;

Then according to the formula Determining a historical data spectral distribution function, wherein/>Sigma is variance and lambda is eigenvalue.

Specifically, the accuracy and the stability of state sensing of a multi-energy system with complex data can be ensured on the basis of ensuring that the time scale problem is fully considered.

Step S2: determining a spectrum difference value calculation formula based on a linear function and a divergence theorem, determining a spectrum difference value of the historical data spectrum distribution function and a theoretical spectrum distribution function as a historical matrix spectrum difference value according to the spectrum difference value calculation formula, and determining a first dynamic threshold gamma ₁ according to the historical matrix spectrum difference value, wherein the first dynamic threshold is used for detecting the system state of the multi-energy system;

the multi-energy system comprises an electric power sub-network, a natural gas sub-network and a thermal sub-network, the multi-energy system comprises three time scales, namely a short time scale, a medium time scale and a long time scale, and the step S2 comprises the following steps:

Step S21, determining a spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T) based on a linear function and a divergence theorem, wherein η is a constant, JS (·) is a JS divergence variation value, P _t ⁱ is a matrix spectrum distribution function at time t, i=e, g, h, e is a covariance matrix spectrum distribution function corresponding to a short-time scale detection matrix, g is a covariance matrix spectrum distribution function corresponding to a medium-time scale detection matrix, h is a covariance matrix spectrum distribution function corresponding to a long-time scale detection matrix, and P ^T is a random matrix theoretical spectrum distribution; according to the spectrum distribution theory, the spectrum distribution function of the covariance matrix can reflect the state of the system, so that the variation degree of the spectrum distribution function of the covariance matrix is described by using the divergence and hyperbolic tangent function, and a corresponding spectrum difference value is obtained.

Step S22, determining the historical matrix spectrum difference values corresponding to the three time scales according to the historical data spectrum distribution function and the spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T)Wherein,Specifically, the spectrum distribution function of the detection matrix corresponding to the historical normal operation data is calculated by using a spectrum difference value calculation formula, and the average value of the spectrum difference values corresponding to three time scales in a period of time is obtained, so that the dynamic error of the state threshold of the detection system can be obtained.

Step S23, determining the first dynamic threshold gamma ₁ according to the historical matrix spectrum difference values and dynamic errors respectively corresponding to the three time scales, wherein,Wherein, the dynamic error is 0.3 in the present example, and in some embodiments, the dynamic error can be adjusted to other values according to different simulation topologies.

Specifically, this can increase the accuracy of the state judgment.

Step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference valueWherein the real-time covariance matrix is related to a time scale;

the process of the step 3 is as follows:

Step 3.1: constructing a matrix X _N∈C_n×T according to the real-time operation data of the distributed system, detecting nodes and sampling time, wherein n is the number of the nodes, T is the sampling time length, and the sampling time T=100deg.S is set in the embodiment;

Step 3.2: since the multi-energy system contains three different energy sources of electricity, gas and heat, in the operation process, there are different time scales, three different time scale matrixes are constructed according to different time scales, wherein in the short-time scale, the matrixes are composed of node voltages of the electric power sub-network, in the medium-time scale, the matrixes are composed of node voltages of the electric power sub-network and node pressures of the natural gas sub-network, in the long-time scale, the matrixes are composed of node voltages of the electric power sub-network, node pressures of the natural gas sub-network and node pressures of the thermal sub-network, and the matrix X _N∈C_n×T is shown as the formula (1),

X_N＝[x_t-T+1,x_t-T+2,…,x_t] (1)

In the method, in the process of the invention,N _e is the number of detection nodes of the electric power sub-network, n _g is the number of detection nodes of the natural gas sub-network, and n _h is the number of detection nodes of the thermal sub-network; in this example, n _e＝33,n_g＝20,n_h =32.

Step 3.3: respectively carrying out normalization treatment on the three matrixes, as shown in formula (2);

Step 3.4: the line vectors are normalized as in equation (3) to obtain a matrix

Step 3.5: then, according to the transformation, obtaining a corresponding covariance matrix, as shown in formula (4),

And a spectral distribution function of the covariance matrix, as in equation (5),

Wherein,

Step 3.6: the utilized formula d _t＝tanh(ηJS(P_t ⁱ||P^T)) to calculate the current spectrum difference value

Step S4: according to the real-time matrix spectrum difference valueDetermining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁, and obtaining a first judgment result;

Specifically, the spectrum difference value of the real-time detection matrix is compared with the dynamic threshold gamma ₁ for judging the system state obtained in the step 2, so that the spectrum difference value of the detection matrix is larger than the current threshold value in 300s, the spectrum difference value is reduced to be lower than the threshold value again in 500s, and the judging result of 300s-500s abnormal state can be obtained.

Wherein, the step S4 includes the following steps:

step S41, wherein the real-time matrix spectrum difference value is If the first dynamic threshold value gamma ₁ is larger than the first dynamic threshold value gamma ₁, determining that the first system state is an abnormal state;

step S42, wherein the real-time matrix spectrum difference value is And determining that the first system state is a non-abnormal state when the first dynamic threshold value gamma ₁ is smaller than or equal to the first dynamic threshold value gamma ₁.

Step S5: calculating the signal-to-noise ratio of the multi-energy system at the current moment, determining a second dynamic threshold gamma ₂ according to the signal-to-noise ratio, and calculating the real-time covariance matrix by adopting a Rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max;

The method for calculating the signal-to-noise ratio of the multi-energy system at the current moment and determining the second dynamic threshold gamma ₂ according to the signal-to-noise ratio comprises the following steps:

step S51, determining a frequency domain signal x _i(e^jw according to the real-time covariance matrix;

step S52, according to the spectral density bandwidth formula Determining a spectral density bandwidth, wherein b is the spectral density bandwidth, I ₀ [. Cndot. ] is a Bessel function, beta is a constant value, [. Cndot. ] _RMS is a root mean square value, and mu (. Cndot.) is a variance;

step S53, according to the frequency domain signal, the spectral density bandwidth and the signal power formula And noise power formula/>Determining a signal power P _s and a noise power P _n;

Step S54, determining the SNR according to the signal power and the noise power, wherein,

Step S55, determining the second dynamic threshold gamma ₂ according to the signal-to-noise ratio, wherein,

The method comprises the following steps of:

step S56, constructing an iterative algorithm formula Wherein v _k is a feature vector corresponding to the maximum feature value of the real-time covariance matrix, a _k is an iteration vector, and a ₀＝{1,1,…,1}_n×1 is initially set;

Step S57, determining an estimated covariance maximum eigenvalue lambda _max according to the iterative algorithm formula, wherein lambda _max≈max(v_k).

Step S6: determining a second system state of the multi-energy system according to the magnitude relation between the estimated covariance maximum eigenvalue lambda _max and the second dynamic threshold gamma ₂, and obtaining a second judgment result;

wherein, the step S6 includes the following steps:

Step S61, determining that the second system state is an abnormal state when the estimated covariance maximum eigenvalue λ _max is greater than the second dynamic threshold value γ ₂;

step S62, determining that the second system state is a non-abnormal state when the estimated covariance maximum eigenvalue λ _max is less than or equal to the second dynamic threshold value γ ₂.

Step S7: and determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system under the condition that the first judging result and the second judging result are the same.

Specifically, the obtained maximum covariance characteristic value is compared with a dynamic threshold gamma ₂, so that a spectrum difference value of the detection matrix is larger than a threshold value at the time when the detection matrix is 300s, and the spectrum difference value is reduced to be lower than the threshold value again when the detection matrix is 500s, and a judging result of 300s-500s abnormal state can be obtained.

From the results, it can be seen that there is an abnormal condition in the system operation of 300s-500 s.

The detection matrix is constructed in a time scale dividing mode, a sub-network using matrix of the multi-energy system is described, and on the basis, the spectrum difference values of the historical detection matrix and the real-time detection matrix are respectively solved to form a dynamic threshold value and a detection value. And then estimating the maximum eigenvalue of the real-time detection matrix by adopting a Rayleigh Li Shang iteration algorithm on the basis, and solving a double dynamic threshold by utilizing a signal-to-noise ratio. The method can ensure the accuracy and stability of state sensing of a multi-energy system with complex data on the basis of ensuring that the time scale problem is fully considered.

According to the distributed multi-energy system state sensing method based on the dual dynamic threshold, a detection matrix formed by system operation data is analyzed by using a matrix spectrum distribution theory, a dual dynamic threshold is formed by using spectrum difference values of historical data, and the spectrum difference values of the detection matrix are calculated to judge. And calculating the signal-to-noise ratio at the current moment, calculating the double dynamic threshold by using the signal-to-noise ratio, and judging by taking the covariance matrix eigenvalue of the large-dimension matrix as the eigenvalue. The detection matrix is divided into short, medium and long time scales so as to increase the accuracy of state judgment, and the problem that the existing system state determining method is only applicable to a single energy system and cannot be applied to a multi-energy system without considering the time scale is solved.

In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the distributed multi-energy system state sensing method based on dual dynamic threshold of the present application will be described in detail below with reference to specific embodiments.

The embodiment relates to a specific distributed multi-energy system state sensing method based on dual dynamic threshold, as shown in fig. 4, including:

Step S101: acquiring historical normal operation data of a distributed system, forming an original matrix of the historical data, obtaining a corresponding covariance matrix through transformation, and calculating to obtain a matrix spectrum distribution function corresponding to the covariance matrix;

Step S102: a calculation formula of a system covariance matrix spectrum difference value is defined by using a linear function and a divergence theorem, a spectrum difference value of a historical data spectrum distribution function and a theoretical spectrum distribution function is calculated, so that a dynamic error of a detection system state threshold value is obtained, and a dynamic threshold value gamma ₁ of the detection system state is further determined;

Step S103: processing the real-time operation data of the system according to the step 101 to obtain a covariance matrix of the real-time data and a corresponding matrix spectrum distribution function, and obtaining a spectrum difference value of the real-time covariance matrix by utilizing the step 102

Step S104: comparing the spectrum difference value of the real-time detection matrix with the dynamic threshold gamma ₁ for judging the system state obtained in the step 102, and detecting the spectrum difference value of the matrix in real timeWhen the dynamic threshold gamma ₁ is larger than the dynamic threshold gamma ₁, judging that the system state is abnormal, otherwise, judging that the system state is normal, and obtaining a judging result 1;

Step S105: calculating the signal-to-noise ratio at the current moment, and calculating a dynamic threshold gamma ₂ by using the signal-to-noise ratio;

Step S106: calculating the real-time data covariance matrix obtained in the step 103 by using a Rayleigh Li Shang iteration algorithm to obtain an estimated covariance maximum eigenvalue lambda _max;

Step S107: comparing the obtained maximum covariance eigenvalue with a dynamic threshold gamma ₂, if the obtained maximum covariance eigenvalue lambda _max is larger than the dynamic threshold gamma ₂, the system state is abnormal, otherwise, the system state is normal, and a judgment result 2 is obtained;

step S108: comparing the judging result 1 with the judging result 2, if the judging result is consistent, outputting the result, and if the judging result is inconsistent, outputting the result to the front end, and judging by a worker.

It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.

The embodiment of the application also provides a distributed multi-energy system state sensing device based on the dual dynamic threshold, and the distributed multi-energy system state sensing device based on the dual dynamic threshold can be used for executing the distributed multi-energy system state sensing method based on the dual dynamic threshold. The device is used for realizing the above embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The following describes a distributed multi-energy system state sensing device based on a dual dynamic threshold.

FIG. 5 is a schematic diagram of a distributed multi-energy system state sensing device based on dual dynamic thresholds according to an embodiment of the present application. As shown in fig. 5, the apparatus includes a first processing unit 10, a second processing unit 20, a third processing unit 30, a fourth processing unit 40, a fifth processing unit 50, a sixth processing unit 60, and a seventh processing unit 70, and the first processing unit 10 is configured to perform step S1: determining a historical data original matrix according to historical operation data of the multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale; the second processing unit 20 is configured to perform step S2: determining a spectrum difference value calculation formula based on a linear function and a divergence theorem, determining a spectrum difference value of the historical data spectrum distribution function and a theoretical spectrum distribution function as a historical matrix spectrum difference value according to the spectrum difference value calculation formula, and determining a first dynamic threshold gamma ₁ according to the historical matrix spectrum difference value, wherein the first dynamic threshold is used for detecting the system state of the multi-energy system; the third processing unit 30 is configured to execute step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference valueWherein the real-time covariance matrix is related to a time scale; the fourth processing unit 40 is configured to perform step S4: according to the real-time matrix spectrum difference value/>Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁, and obtaining a first judgment result; the fifth processing unit 50 is configured to execute step S5: calculating the signal-to-noise ratio of the multi-energy system at the current moment, determining a second dynamic threshold gamma ₂ according to the signal-to-noise ratio, and calculating the real-time covariance matrix by adopting a Rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max; the sixth processing unit 60 is configured to execute step S6: determining a second system state of the multi-energy system according to the magnitude relation between the estimated covariance maximum eigenvalue lambda _max and the second dynamic threshold gamma ₂, and obtaining a second judgment result; the seventh processing unit 70 is configured to perform step S7: and determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system under the condition that the first judging result and the second judging result are the same.

The distributed multi-energy system state sensing device based on the dual dynamic threshold value uses a matrix spectrum distribution theory to analyze a detection matrix formed by system operation data, forms a dual dynamic threshold value by using the spectrum difference value of historical data, and obtains the spectrum difference value of the detection matrix to judge. And calculating the signal-to-noise ratio at the current moment, calculating the double dynamic threshold by using the signal-to-noise ratio, and judging by taking the covariance matrix eigenvalue of the large-dimension matrix as the eigenvalue. The detection matrix is divided into short, medium and long time scales so as to increase the accuracy of state judgment, and the problem that the existing system state determining method is only applicable to a single energy system and cannot be applied to a multi-energy system without considering the time scale is solved.

In some optional examples, the multi-energy system includes an electric sub-network, a natural gas sub-network, and a thermal sub-network, the first processing unit includes a first acquisition module, a first construction module, a normalization processing module, and a first determination module, where the first acquisition module is configured to acquire historical operation data of the multi-energy system in a historical time period, and three time scales, where the three time scales are a short time scale, a medium time scale, and a long time scale, respectively; the first construction module is configured to construct three different time scale matrices X _H∈C_n×T based on the three time scales, the corresponding detection nodes of the multi-energy system, and the corresponding sampling time, where X _H＝[x_t-T+1,x_t-T+2,…,x_t, T is the sampling time length,N _e is the number of detection nodes of the electric power sub-network, n _g is the number of detection nodes of the natural gas sub-network, and n _h is the number of detection nodes of the thermal sub-network; the normalization processing module is used for respectively carrying out normalization processing on the three time scale matrixes to obtain a normalization processing matrix/>The normalization processing module is used for performing normalization processing on the row vectors of the normalization processing matrix to obtain a normalization processing matrix/>The first determining module is used for performing target transformation on the normalization processing matrix and the normalization processing matrix to obtain the historical covariance matrix and the historical data spectrum distribution function. Therefore, on the basis of guaranteeing that the time scale problem is fully considered, the accuracy and the stability of state sensing of a multi-energy system with complex data are guaranteed.

In some optional examples, the multi-energy system includes an electric sub-network, a natural gas sub-network, and a thermal sub-network, where the multi-energy system includes three time scales, which are a short time scale, a medium time scale, and a long time scale, the first processing unit includes a second determining module, a third determining module, and a fourth determining module, where the second determining module is configured to determine a spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T) based on a linear function and a divergence theorem, where η is a constant, JS (·) is a JS divergence variation value, P _t ⁱ is a matrix spectrum distribution function at time t, i=e, g, h, e is a covariance matrix spectrum distribution function corresponding to a short time scale detection matrix, g is a covariance matrix spectrum distribution function corresponding to a medium time scale detection matrix, h is a covariance matrix spectrum distribution function corresponding to a long time scale detection matrix, and P ^T is a random matrix theoretical spectrum distribution; the third determining module is configured to determine the historical matrix spectrum difference values corresponding to the three time scales according to the historical data spectrum distribution function and the spectrum difference value calculation d _t＝tanh(ηJS(P_t ⁱ||P^T)Wherein,The fourth determining module is configured to determine the first dynamic threshold γ ₁ according to the historical matrix spectrum difference values and dynamic errors respectively corresponding to the three time scales, where/>This can increase the accuracy of the state judgment.

In this embodiment, the fourth processing unit includes a fifth determining module and a sixth determining module, where the fifth determining module is configured to determine the real-time matrix spectrum difference valueIf the first dynamic threshold value gamma ₁ is larger than the first dynamic threshold value gamma ₁, determining that the first system state is an abnormal state; the sixth determining module is used for the real-time matrix spectrum difference value/>And determining that the first system state is a non-abnormal state when the first dynamic threshold value gamma ₁ is smaller than or equal to the first dynamic threshold value gamma ₁. Therefore, on the basis of guaranteeing that the time scale problem is fully considered, the accuracy and the stability of state sensing of a multi-energy system with complex data are guaranteed.

In some optional examples, the fifth processing unit includes a seventh determining module, an eighth determining module, a ninth determining module, a tenth determining module, and an eleventh determining module, where the seventh determining module is configured to determine the frequency domain signal x _i(e^jw according to the real-time covariance matrix; the eighth determination module is used for determining the bandwidth formula according to the spectral densityDetermining a spectral density bandwidth, wherein b is the spectral density bandwidth, I ₀ [. Cndot. ] is a Bessel function, beta is a constant value, [. Cndot. ] _RMS is a root mean square value, and mu (. Cndot.) is a variance; a ninth determination module for determining a signal power formula according to the frequency domain signal, the spectral density bandwidth, and the signal power formulaAnd noise power formula/>Determining a signal power P _s and a noise power P _n; a tenth determination module for determining the signal-to-noise ratio SNR based on the signal power and the noise power, wherein/>An eleventh determination module is configured to determine the second dynamic threshold γ ₂ according to the signal-to-noise ratio, where/>Therefore, on the basis of guaranteeing that the time scale problem is fully considered, the accuracy and the stability of state sensing of a multi-energy system with complex data are guaranteed. /(I)

Alternatively, the fifth processing unit includes a second building module and a twelfth determining module, where the second building module is configured to build an iterative algorithm formulaWherein v _k is a feature vector corresponding to the maximum feature value of the real-time covariance matrix, a _k is an iteration vector, and a ₀＝{1,1,…,1}_n×1 is initially set; the twelfth determination module is configured to determine an estimated covariance maximum eigenvalue λ _max according to the iterative algorithm formula, where λ _max≈max(v_k). Therefore, on the basis of guaranteeing that the time scale problem is fully considered, the accuracy and the stability of state sensing of a multi-energy system with complex data are guaranteed.

As an alternative, the sixth processing unit includes a thirteenth determining module and a fourteenth determining module, where the thirteenth determining module is configured to determine that the second system state is an abnormal state if the estimated covariance maximum eigenvalue λ _max is greater than the second dynamic threshold value γ ₂; the fourteenth determining module is configured to determine that the second system state is a non-abnormal state if the estimated covariance maximum eigenvalue λ _max is less than or equal to the second dynamic threshold γ ₂. Therefore, on the basis of guaranteeing that the time scale problem is fully considered, the accuracy and the stability of state sensing of a multi-energy system with complex data are guaranteed.

The distributed multi-energy system state sensing device based on the dual dynamic threshold comprises a processor and a memory, wherein the first processing unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; or the above modules may be located in different processors in any combination.

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one, and the problem that the existing system state determining method can only be applied to a single-energy system but not to a multi-energy system without considering time scale is solved by adjusting kernel parameters.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein the device where the computer readable storage medium is located is controlled to execute the distributed multi-energy system state sensing method based on the dual dynamic threshold when the program runs.

Specifically, the distributed multi-energy system state sensing method based on the dual dynamic threshold value comprises the following steps:

step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference value Wherein the real-time covariance matrix is related to a time scale;

step S4: according to the real-time matrix spectrum difference value Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁, and obtaining a first judgment result;

The embodiment of the invention provides a processor, which is used for running a program, wherein the distributed multi-energy system state sensing method based on the dual dynamic threshold is executed when the program runs.

The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program: the device herein may be a server, PC, PAD, cell phone, etc.

The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with at least the following method steps:

It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.

From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

1) According to the distributed multi-energy system state sensing method based on the dual dynamic threshold, a matrix spectrum distribution theory is used for analyzing a detection matrix formed by system operation data, a dual dynamic threshold is formed by using spectrum difference values of historical data, and the spectrum difference values of the detection matrix are calculated to judge. And calculating the signal-to-noise ratio at the current moment, calculating the double dynamic threshold by using the signal-to-noise ratio, and judging by taking the covariance matrix eigenvalue of the large-dimension matrix as the eigenvalue. The detection matrix is divided into short, medium and long time scales so as to increase the accuracy of state judgment, and the problem that the existing system state determining method is only applicable to a single energy system and cannot be applied to a multi-energy system without considering the time scale is solved.

2) The distributed multi-energy system state sensing device based on the dual dynamic threshold value uses a matrix spectrum distribution theory to analyze a detection matrix formed by system operation data, forms a dual dynamic threshold value by using spectrum difference values of historical data, and obtains the spectrum difference values of the detection matrix to judge. And calculating the signal-to-noise ratio at the current moment, calculating the double dynamic threshold by using the signal-to-noise ratio, and judging by taking the covariance matrix eigenvalue of the large-dimension matrix as the eigenvalue. The detection matrix is divided into short, medium and long time scales so as to increase the accuracy of state judgment, and the problem that the existing system state determining method is only applicable to a single energy system and cannot be applied to a multi-energy system without considering the time scale is solved.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A distributed multi-energy system state sensing method based on dual dynamic thresholds, comprising:

Step S1: determining a historical data original matrix according to historical operation data of a multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale;

Step S4: according to the real-time matrix spectrum difference value Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁ to obtain a first judgment result;

Step S6: determining a second system state of the multi-energy system according to the magnitude relation between the maximum eigenvalue lambda _max of the estimated covariance and the second dynamic threshold gamma ₂, and obtaining a second judgment result;

Step S7: and under the condition that the first judging result and the second judging result are the same, determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system.

2. The state sensing method according to claim 1, wherein the multi-energy system includes an electric power sub-network, a natural gas sub-network, and a thermal sub-network, and the step S1 includes:

Acquiring historical operation data of the multi-energy system in a historical time period and three time scales, wherein the three time scales are respectively a short time scale, a medium time scale and a long time scale;

Constructing three different time scale matrices X _H∈C_n×T based on the three time scales, the corresponding detection nodes of the multi-energy system and the corresponding sampling time, wherein X _H＝[x_t-T+1,x_t-T+2,…,x_t and T are the sampling time lengths, N _e is the number of detection nodes of the electric power sub-network, n _g is the number of detection nodes of the natural gas sub-network, and n _h is the number of detection nodes of the thermal sub-network;

Respectively carrying out normalization processing on the three time scale matrixes to obtain a normalization processing matrix

Performing standardization processing on the row vectors of the normalization processing matrix to obtain a standardization processing matrix

And carrying out target transformation on the normalization processing matrix and the normalization processing matrix to obtain the historical covariance matrix and the historical data spectrum distribution function.

3. The state sensing method according to claim 1, wherein the multi-energy system comprises an electric power sub-network, a natural gas sub-network and a thermal sub-network, the multi-energy system comprises three time scales, a short time scale, a medium time scale and a long time scale, respectively, the step S2 comprises:

Determining a spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T) based on a linear function and a divergence theorem, wherein eta is a constant, JS (·) is a JS divergence variation value, P _t ⁱ is a matrix spectrum distribution function at a time t, i=e, g, h, e is a covariance matrix spectrum distribution function corresponding to a short-time scale detection matrix, g is a covariance matrix spectrum distribution function corresponding to a medium-time scale detection matrix, h is a covariance matrix spectrum distribution function corresponding to a long-time scale detection matrix, and P ^T is a random matrix theoretical spectrum distribution;

According to the historical data spectrum distribution function and the spectrum difference value calculation formula d _t＝tanh(ηJS(P_t ⁱ||P^T)), determining the historical matrix spectrum difference values respectively corresponding to the three time scales Wherein,

Determining the first dynamic threshold gamma ₁ according to the historical matrix spectrum difference values and dynamic errors respectively corresponding to the three time scales, wherein,

4. The state sensing method according to claim 1, wherein the step S4 includes:

Spectral difference values in said real-time matrix If the first dynamic threshold value gamma ₁ is larger than the first dynamic threshold value gamma ₁, determining that the first system state is an abnormal state;

Spectral difference values in said real-time matrix And under the condition of being smaller than or equal to the first dynamic threshold gamma ₁, determining that the first system state is a non-abnormal state.

5. The state sensing method according to claim 1, wherein calculating a signal-to-noise ratio of the multi-energy system at a current time and determining a second dynamic threshold γ ₂ according to the signal-to-noise ratio comprises:

determining a frequency domain signal x _i(e^jw according to the real-time covariance matrix);

According to the bandwidth formula of the spectral density Determining a spectral density bandwidth, wherein b is the spectral density bandwidth, I ₀ [. Cndot. ] is a Bessel function, beta is a constant value, [. Cndot. ] _RMS is a root mean square value, and μ (. Cndot.) is a variance;

according to the frequency domain signal, the spectral density bandwidth and the signal power formula And noise power formula/>Determining a signal power P _s and a noise power P _n;

determining the signal-to-noise ratio SNR based on the signal power and the noise power, wherein,

Determining the second dynamic threshold gamma ₂ from the signal-to-noise ratio, wherein,

6. The state sensing method according to claim 1, wherein calculating the real-time covariance matrix by using a rayleigh Li Shang iteration algorithm to obtain an estimated covariance maximum eigenvalue λ _max comprises:

construction of iterative algorithm formula Wherein v _k is a feature vector corresponding to the maximum feature value of the real-time covariance matrix, alpha _k is an iteration vector, and the initial value is alpha ₀＝{1,1,…,1}n_×1;

According to the iterative algorithm formula, an estimated covariance maximum eigenvalue lambda _max is determined, wherein lambda _max≈max(v_k).

7. The state sensing method according to claim 1, wherein the step S6 includes:

Determining that the second system state is an abnormal state if the estimated covariance maximum eigenvalue λ _max is greater than the second dynamic threshold value γ ₂;

And determining that the second system state is a non-abnormal state in the case that the estimated covariance maximum eigenvalue lambda _max is less than or equal to the second dynamic threshold gamma ₂.

8. A distributed multi-energy system state sensing device based on dual dynamic thresholds, comprising:

A first processing unit, configured to execute step S1: determining a historical data original matrix according to historical operation data of a multi-energy system, determining a historical covariance matrix corresponding to the historical data original matrix, and determining a matrix spectrum distribution function corresponding to the historical covariance matrix as a historical data spectrum distribution function, wherein the historical covariance matrix is related to a time scale;

A second processing unit, configured to execute step S2: determining a spectrum difference value calculation formula based on a linear function and a divergence theorem, determining a spectrum difference value of the historical data spectrum distribution function and a theoretical spectrum distribution function as a historical matrix spectrum difference value according to the spectrum difference value calculation formula, and determining a first dynamic threshold gamma ₁ according to the historical matrix spectrum difference value, wherein the first dynamic threshold is used for detecting the system state of the multi-energy system;

A third processing unit, configured to execute step S3: processing the real-time operation data of the multi-energy system by adopting the method of the step S1, determining a real-time covariance matrix and a corresponding real-time data spectrum distribution function, processing the real-time data spectrum distribution function by adopting the step S2, and determining a real-time matrix spectrum difference value Wherein the real-time covariance matrix is related to a time scale;

a fourth processing unit for executing step S4: according to the real-time matrix spectrum difference value Determining a first system state of the multi-energy system according to the magnitude relation between the first dynamic threshold gamma ₁ and the first dynamic threshold gamma ₁ to obtain a first judgment result;

a fifth processing unit, configured to execute step S5: calculating the signal-to-noise ratio of the multi-energy system at the current moment, determining a second dynamic threshold gamma ₂ according to the signal-to-noise ratio, and calculating the real-time covariance matrix by adopting a Rayleigh Li Shang iterative algorithm to obtain an estimated covariance maximum eigenvalue lambda _max;

A sixth processing unit, configured to execute step S6: determining a second system state of the multi-energy system according to the magnitude relation between the maximum eigenvalue lambda _max of the estimated covariance and the second dynamic threshold gamma ₂, and obtaining a second judgment result;

A seventh processing unit, configured to perform step S7: and under the condition that the first judging result and the second judging result are the same, determining the system state represented by the first judging result and the second judging result as the final system state of the multi-energy system.

9. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run controls a device in which the computer readable storage medium is located to perform the distributed multi-energy system state sensing method based on a dual dynamic threshold according to any one of claims 1 to 7.

10. An electronic device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing the dual dynamic threshold-based distributed multi-energy system state awareness method of any of claims 1-7.