CN110137946B - Data-driven electric power system disturbance space-time feature extraction method - Google Patents
Data-driven electric power system disturbance space-time feature extraction method Download PDFInfo
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- CN110137946B CN110137946B CN201910388536.8A CN201910388536A CN110137946B CN 110137946 B CN110137946 B CN 110137946B CN 201910388536 A CN201910388536 A CN 201910388536A CN 110137946 B CN110137946 B CN 110137946B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention relates to a data-driven method for extracting the disturbance space-time characteristics of an electric power system, which is characterized by comprising the following steps: the establishment of characteristic quantization indexes, the quantization description of voltage dynamic space-time distribution characteristics, the revealing of the dynamic space-time distribution characteristics of the voltage based on measured data and other contents gradually get rid of the dependence on the traditional modeling and simulation, and all the contents take the space-time correlation characteristics of the actual response information of the power grid as the core to realize the rapid analysis and mining of the dynamic information of the large power grid. The dynamic process of the power system after disturbance can be visually and clearly described, and a basis is provided for dispatching personnel, so that the power system can safely and stably operate. Has the advantages of scientific and reasonable structure, strong applicability, good effect and the like.
Description
Technical Field
The invention relates to the technical field of dynamic analysis of an electric power system, in particular to a data-driven method for extracting the disturbance space-time characteristics of the electric power system.
Background
The interconnection range of a power grid and the power generation scale of new energy resources are continuously expanded, the uncertainty and complexity of the operation environment of the power grid are increased, the dynamic behavior of a power system after disturbance is more and more complex, and a large-area power failure accident occurs at home and abroad in recent years, so that huge economic loss and adverse social influence are caused. Therefore, the research on the dynamic characteristics of the power system after disturbance has important significance on dynamic safety analysis, system operation control, disturbance analysis and the like of the power system.
The most common method for researching the dynamic characteristics of the voltage of the power system is numerical simulation, but the numerical simulation analysis method needs to establish a detailed differential algebraic equation system for all elements of the system and gradually solve the system through the numerical method. Therefore, errors caused by model parameters and calculation methods are difficult to avoid, and the Wide-Area Measurement System (WAMS) is gradually popularized and widely applied to an electric power System, so that favorable conditions are created for the development of power grid dynamic process monitoring, voltage stability analysis, state estimation, Wide-Area control and the like based on measured data.
Because each synchronous Phasor Measurement Unit (PMU) measurement information has autocorrelation and inertia, and because topological connection and electromagnetic action relation exist objectively in a power grid, the PMU measurement information has direct or indirect relevance. The wide-area space-time measurement information has structural and relevance characteristics of big data, and dynamic information of the power system is directly mined from the data, so that the method is more convenient and effective.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the data-driven method for extracting the disturbance space-time characteristics of the power system overcomes the defects of the prior art, is scientific and reasonable, has strong applicability and good effect, can visually and clearly describe the dynamic process of the power system after disturbance, provides basis for scheduling personnel and ensures the safe and stable operation of the power system.
The technical scheme for solving the technical problem is that the method for extracting the disturbance space-time characteristics of the data-driven power system is characterized by comprising the following steps:
1) establishment of characteristic quantization index
Average rate of change of voltage
kv＝(U0-Umin)/(t0-tmin) (1)
In the formula: u shape0,UminRespectively, an initial value of the voltage and a lowest value, t0,tminFor times corresponding to the initial value and the lowest value of the voltage, kvRepresents the average rate of change of voltage;
maximum relative change of voltage
ΔUmax＝[Δumax1,Δumax2,...,Δumaxn] (2)
In the formula: Δ umaxi( i 1, 2.. n.) is the maximum relative change in voltage at observation point i, U0iIs an initial value of voltage, UminiIs the lowest point voltage value;
③ Disturbance Contribution Index (DCI)
For a voltage data set, the covariance matrix is calculated using N of the data samples, and the expression is:
in the formula CsysRepresenting covariance, G being collected voltage data;
for a matrix Z of dimension n × m, its energy is represented by the Frobenius norm as:
wherein E (Z) represents norm, zijA matrix representing n × m;
Csysthe norm of (a) is the total energy produced by the system, n is the number of measurement points, m is the number of time series of data, and the same covariance matrix for the ith observation point is expressed as:
the total energy of the data set in each disturbance is obtained through the Frobenius norm of the formula (5), so that the ratio of the energy content of the ith measurement point to the energy content of the whole system is used as an index for measuring the contribution degree of the ith observation point to the disturbance of the whole system, and is called as a Disturbance Contribution Index (DCI) of the i observation point, and the expression is as follows:
DCI is expressed as a perturbation contribution index, E (C)i) Representing the total energy per perturbation, E (C)sys) Representing the total energy of the disturbance;
response delay
The response time of each observation point is represented by a set of time series:
Tr＝[tr1,tr2,...,trn] (8)
in the formula tri( i 1, 2.., n) represents the response time at observation point i;
if the response time is determined, the time t at which the disturbance occurs0It is also determined that the delay time of each observation point to the disturbance can be determined:
ΔTr＝[tr1-t0,tr2-t0,...,trn-t0] (9)
frequency maximum offset
The maximum value of the absolute value of the frequency offset of each node is expressed as:
ΔF＝[Δfmax1,Δfmax2,...,Δfmaxn] (10)
wherein Δ fmaxi( i 1, 2.. n) is the maximum frequency offset at observation point i;
2) power system dynamic space-time distribution characteristic quantitative description
For an observation data set formed by n observation points, an n multiplied by 5 space-time characteristic index description matrix is formed by voltage average change rate, voltage maximum relative change quantity, disturbance contribution index, response delay and frequency maximum offset characteristic quantity:
wherein: k is a radical ofiIs the average rate of change of the voltage at observation point i; Δ triDelaying the voltage response at observation point i; Δ umaxiThe maximum relative change of the voltage at the observation point i; Δ fmaxiThe maximum change of the frequency at the observation point i; DCIiContributing an index to the disturbance at observation point i;
through the index matrix D, the change situation of each index value effectively reflects the change trend of the electric quantity of each observation point after disturbance, and the dynamic space-time distribution characteristics of the power system can be quantitatively analyzed from a multidimensional angle.
3) Revealing dynamic time-space distribution characteristics of voltage based on measured data
(1) And selecting power grid data of a certain area, and analyzing the time sequence and the spatial characteristics of the voltage. And obtaining the voltage distribution condition of each observation point in the disturbance process. By comparing the voltage change characteristics of each node, the relation between the overall voltage level and the electrical distance and the influence degree of disturbance after the fault is analyzed.
(2) Dynamic space-time distribution characteristic of power system based on comprehensive index quantitative analysis
Establishing an observation data set, wherein the observation data set should cover the whole power grid as much as possible, and meanwhile, the redundancy of data is also considered; and defining the electrical distance between the observation point and the fault point as the observation distance, and analyzing each index by calculating the obtained voltage dynamic characteristic index.
The data-driven method for extracting the disturbance space-time characteristics of the power system can visually and clearly describe the dynamic process of the power system after disturbance, provide a basis for scheduling personnel and ensure that the power system safely and stably operates. Has the advantages of scientific and reasonable structure, strong applicability, good effect and the like.
Drawings
FIG. 1 is a graph of spatio-temporal distribution characteristics;
FIG. 2 is a graph of average rate of change of voltage versus electrical distance;
FIG. 3 is a graph of maximum relative change in voltage versus electrical distance;
FIG. 4 is a graph of response delay versus electrical distance;
FIG. 5 is a plot of disturbance contribution index versus electrical distance;
fig. 6 is a graph showing the maximum relative voltage change amount.
Detailed Description
The present invention will be described in detail below with reference to the drawings and examples.
The invention discloses a data-driven method for extracting the disturbance space-time characteristics of an electric power system, which comprises the following steps:
1) establishment of characteristic quantization index
Average rate of change of voltage
kv＝(U0-Umin)/(t0-tmin) (1)
In the formula: u shape0,UminRespectively, an initial value of the voltage and a lowest value, t0,tminFor times corresponding to the initial value and the lowest value of the voltage, kvRepresents the average rate of change of voltage;
maximum relative change of voltage
ΔUmax＝[Δumax1,Δumax2,...,Δumaxn] (2)
In the formula: Δ umaxi( i 1, 2.. n.) is the maximum relative change in voltage at observation point i, U0iIs an initial value of voltage, UminiIs the lowest point voltage value;
③ Disturbance Contribution Index (DCI)
For a voltage data set, the covariance matrix is calculated using N of the data samples, and the expression is:
in the formula CsysRepresenting covariance, G being collected voltage data;
for a matrix Z of dimension n × m, its energy is represented by the Frobenius norm as:
wherein E (Z) represents norm, zijA matrix representing n × m;
Csysis the total energy produced by the system, n isThe number of measurement points, m, is the number of time series of data, and the same covariance matrix for the ith observation point is given by:
the total energy of the data set in each disturbance is obtained through the Frobenius norm of the formula (5), so that the ratio of the energy content of the ith measurement point to the energy content of the whole system is used as an index for measuring the contribution degree of the ith observation point to the disturbance of the whole system, and is called as a Disturbance Contribution Index (DCI) of the i observation point, and the expression is as follows:
DCI is expressed as a perturbation contribution index, E (C)i) Representing the total energy per perturbation, E (C)sys) Representing the total energy of the disturbance;
response delay
The response time of each observation point is represented by a set of time series:
Tr＝[tr1,tr2,...,trn] (8)
in the formula tri( i 1, 2.., n) represents the response time at observation point i;
if the response time is determined, the time t at which the disturbance occurs0It is also determined that the delay time of each observation point to the disturbance can be determined:
ΔTr＝[tr1-t0,tr2-t0,...,trn-t0] (9)
frequency maximum offset
The maximum value of the absolute value of the frequency offset of each node is expressed as:
ΔF＝[Δfmax1,Δfmax2,...,Δfmaxn] (10)
wherein Δ fmaxi( i 1, 2.. n.) is the maximum at observation point iA frequency offset;
2) power system dynamic space-time distribution characteristic quantitative description
For an observation data set formed by n observation points, an n multiplied by 5 space-time characteristic index description matrix is formed by voltage average change rate, voltage maximum relative change quantity, disturbance contribution index, response delay and frequency maximum offset characteristic quantity:
wherein: k is a radical ofiIs the average rate of change of the voltage at observation point i; Δ triDelaying the voltage response at observation point i; Δ umaxiThe maximum relative change of the voltage at the observation point i; Δ fmaxiThe maximum change of the frequency at the observation point i; DCIiContributing an index to the disturbance at observation point i;
through the index matrix D, the change situation of each index value effectively reflects the change trend of the electric quantity of each observation point after disturbance, and the dynamic space-time distribution characteristics of the power system can be quantitatively analyzed from a multidimensional angle.
3) Revealing dynamic space-time distribution characteristics of power system based on measured data
(1) And selecting power grid data of a certain area, and analyzing the time sequence and the spatial characteristics of the voltage. And obtaining the voltage distribution condition of each observation point in the disturbance process. Fig. 1 shows the voltage distribution of each observation point in the disturbance process, and it can be known through the analysis of the measured data of different nodes that: when the voltage of the fault point is reduced, the overall voltage of the power grid is also reduced, and the reduced amplitude of the voltage is reduced along with the increase of the distance from the disturbance point.
(2) Dynamic space-time distribution characteristic of power system based on comprehensive index quantitative analysis
And establishing an observation data set, wherein the observation data set should cover the whole power grid as much as possible, and meanwhile, the redundancy of the data is also considered. The electrical distance between the observation point and the fault point is defined as the observation distance, and the dynamic characteristic index description matrix obtained by calculation is shown in table 1.
TABLE 1 calculation results of observation point specific information and characteristic quantity index
From the results in the above table, it can be seen that after the disturbance occurs, the average change rate of the voltage drop at each observation point is obviously different: the maximum and minimum are approximately 3 times different. The disturbance contribution index near the fault point is very large and is 0.393, and the maximum and minimum relative change amounts of the voltage are different by about 4 times. Fig. 2 to 3 show that the longer the electrical distance from the fault point, the overall trend of the average change rate of the measured voltage increases, the overall decreasing delay time tends to increase, and the relative offset of the maximum voltage gradually decreases. The degree of disturbance is progressively weaker as the electrical distance increases, but the duration is substantially the same. It is derived from fig. 5 that the contribution index to the disturbance beyond a certain distance is 0, and the disturbance contribution is mainly concentrated in a certain area. Through the analysis and description of the characteristic indexes, the dynamic characteristics of the voltage can be obviously expressed.
The dynamic change of the power system after disturbance is reflected by establishing reliable indexes, and the indexes have certain monotonicity. The propagation process of the disturbance and the influence degree of the disturbance in different areas can be clearly reflected through the graph 6, and the safety analysis of the power system is facilitated. The indexes can present the space-time distribution characteristics of the power system from different dimensions, and extract information from the measured data, so that the real dynamic behavior of the power system is reflected.
Claims (1)
1. A data-driven method for extracting the disturbance space-time characteristics of an electric power system is characterized by comprising the following steps:
1) establishment of characteristic quantization index
Average rate of change of voltage
kv＝(U0-Umin)/(t0-tmin) (1)
In the formula: u shape0,UminAre respectively a voltage primaryStarting and lowest values, t0,tminFor times corresponding to the initial value and the lowest value of the voltage, kvRepresents the average rate of change of voltage;
maximum relative change of voltage
△Umax＝[△umax1,△umax2,...,△umaxn] (2)
In the formula: delta umaxi(i 1, 2.. n.) is the maximum relative change in voltage at observation point i, U0iIs an initial value of voltage, UminiIs the lowest point voltage value;
③ Disturbance Contribution Index (DCI)
For a voltage data set, the covariance matrix is calculated using N of the data samples, and the expression is:
in the formula CsysRepresenting covariance, G being collected voltage data;
for a matrix Z of dimension n × m, its energy is represented by the Frobenius norm as:
wherein E (Z) represents norm, zijA matrix representing n × m;
Csysthe norm of (a) is the total energy produced by the system, n is the number of measurement points, m is the number of time series of data, and the same covariance matrix for the ith observation point is expressed as:
the total energy of the data set in each disturbance is obtained through the Frobenius norm of the formula (5), so that the ratio of the energy content of the ith measurement point to the energy content of the whole system is used as an index for measuring the contribution degree of the ith observation point to the disturbance of the whole system, and is called as a Disturbance Contribution Index (DCI) of the i observation point, and the expression is as follows:
DCI is expressed as a perturbation contribution index, E (C)i) Representing the total energy per perturbation, E (C)sys) Representing the total energy of the disturbance;
response delay
The response time of each observation point is represented by a set of time series:
Tr＝[tr1,tr2,...,trn] (8)
in the formula tri(i 1, 2.., n) represents the response time at observation point i;
if the response time is determined, the time t at which the disturbance occurs0It is also determined that the delay time of each observation point to the disturbance can be determined:
△Tr＝[tr1-t0,tr2-t0,...,trn-t0] (9)
frequency maximum offset
The maximum value of the absolute value of the frequency offset of each node is expressed as:
△F＝[△fmax1,△fmax2,...,△fmaxn] (10)
wherein Δ fmaxi(i 1, 2.. n.) is the maximum frequency offset at observation point i
2) Power system dynamic space-time distribution characteristic quantitative description
For an observation data set formed by n observation points, an n multiplied by 5 space-time characteristic index description matrix is formed by voltage average change rate, voltage maximum relative change quantity, disturbance contribution index, response delay and frequency maximum offset characteristic quantity:
wherein: k is a radical ofiIs the average rate of change of the voltage at observation point i; delta triDelaying the voltage response at observation point i; delta umaxiThe maximum relative change of the voltage at the observation point i; DCIiContributing an index to the disturbance at observation point i;
through the index matrix D, the change situation of the index values effectively reflects the change trend of the electric quantity of each observation point after disturbance, and the dynamic space-time distribution characteristics of the power system can be quantitatively analyzed from a multidimensional angle;
3) revealing dynamic space-time distribution characteristics of power system based on measured data
(1) Selecting power grid data of a certain area, and analyzing the time sequence and the spatial characteristics of the voltage to obtain the voltage distribution condition of each observation point in the disturbance process; by comparing the voltage change characteristics of each node, the relation between the overall voltage level and the influence degree of the disturbance, which is how the overall voltage level changes after the fault, is analyzed;
(2) dynamic space-time distribution characteristic of power system based on comprehensive index quantitative analysis
Establishing an observation data set, wherein the observation data set should cover the whole power grid as much as possible, and meanwhile, the redundancy of data is also considered; and defining the electrical distance between the observation point and the fault point as the observation distance, and analyzing each index by calculating the obtained voltage dynamic characteristic index.
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