CN110428164B - Power grid running state monitoring method, device and equipment and readable storage medium - Google Patents
Power grid running state monitoring method, device and equipment and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method for monitoring the running state of a power grid, which comprises the following steps: transforming each sample point in a pre-acquired sample point matrix to an original domain by using an input probability model established by an NPNT algorithm to obtain each initial wind speed value and each initial load value, and inputting the initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result; generating a preset number of sample points in an unrepresented probability space of the sample point matrix, and converting the sample points to an original domain to obtain each newly added wind speed value and each newly added load value; obtaining an updated load flow calculation result by utilizing an ILHS algorithm; if the convergence condition is not reached, generating a preset number of sample points in an unrepresented probability space of the sample point matrix; and if so, finishing monitoring the running state of the power grid. The balance of the power grid probability power flow analysis precision and efficiency is realized, and the effective monitoring of the running state of the power grid is realized. The invention also discloses a device, equipment and a storage medium for monitoring the running state of the power grid, and the device, the equipment and the storage medium have corresponding technical effects.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method, a device and equipment for monitoring the running state of a power grid and a computer readable storage medium.
Background
With the increasing operation and construction of the VSC-MTDC system, the scale of the power grid will be continuously expanded, and the number of uncertain sources will also increase sharply. These sources of uncertainty are distributed over a wide geographic space and may be influenced by a variety of complex factors (e.g., geographic conditions, meteorological factors, etc.), which may be subject to arbitrary distributions and correlations between distributions. Therefore, the established input probability model needs to have the capability of processing random variables to obey arbitrary distribution and have correlation among the distributions. However, in probabilistic power flow calculations, the source of uncertainty is often assumed to be a common distribution, e.g. the load is generally considered to be a Normal distribution, and the wind speed is often modeled with a Weibull distribution. Meanwhile, the NATAF transformation is used for establishing the correlation among different distributions. In a few operation scenarios, the input probability model may have a certain effectiveness. However, wind speed and load in an actual grid do not necessarily follow a common distribution, but are most likely to follow a very common distribution. Then, the input probability model based on the common distribution will cause the probability trend analysis to generate huge errors.
Commonly used probabilistic power flow algorithms include three categories: analytical methods, approximation methods, and Monte Carlo Simulation (MCS). The analytical methods are high in calculation efficiency, but most of the analytical methods linearize the nonlinear model and cannot deal with the correlation between random variables, so that the calculation accuracy is difficult to satisfy. The basic idea of the approximation method is to approximate the input probability distribution by carefully selecting sample points of the input probability distribution, but the approximation method cannot directly obtain a Probability Density Function (PDF) of the probability power flow analysis result. This will make it difficult to analyze the probabilistic power flow results in depth.
The Monte Carlo simulation method is generally able to provide "correct" simulation results, which are often used to verify the validity of other algorithms. However, the probabilistic power flow analysis based on the monte carlo simulation method is extremely time-consuming. A traditional Latin hypercube sampling technology (CLHS) based on a Monte Carlo simulation method generates sample points in a layered mode, can cover input probability distribution in a wider range, and achieves the purpose of improving the efficiency and precision of probability power flow analysis. Meanwhile, the traditional Latin hypercube sampling technology has the capability of outputting moment information and a probability density function. However, a drawback of conventional latin hypercube sampling techniques is that their highly structured set of sample points makes it difficult to directly add additional sample points. If the traditional Latin hypercube sampling technology directly increases sample points, the formed new sample point set (containing the original sample points and the newly increased sample points) is difficult to maintain the original layering characteristic, and the computing efficiency of the traditional Latin hypercube sampling technology is reduced. Meanwhile, because the traditional latin hypercube sampling technology is difficult to increase sample points, a problem arises when the traditional latin hypercube sampling technology is used for carrying out probability power flow analysis on the power grid, namely, how many sample points are selected to be suitable for analyzing the power grid.
In summary, how to effectively solve the problems that the existing power grid operation state analysis mode cannot balance the power grid probability power flow analysis precision and efficiency, and therefore cannot effectively monitor the operation state of the power grid, and the like, is a problem that needs to be solved by technicians in the field at present.
Disclosure of Invention
The invention aims to provide a method for monitoring the running state of a power grid, which realizes the balance of the analysis precision and the efficiency of the probability power flow of the power grid, thereby realizing the effective monitoring of the running state of the power grid; another object of the present invention is to provide a device, an apparatus and a computer readable storage medium for monitoring the operation state of a power grid.
In order to solve the technical problems, the invention provides the following technical scheme:
a power grid operation state monitoring method comprises the following steps:
when a power grid operation state monitoring instruction is received, obtaining an initial number of sample points in uniform distribution to obtain a sample point matrix, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm to obtain each initial wind speed value and each initial load value obeying arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data;
inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result;
generating a preset number of sample points in an unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and transforming the newly generated sample points to the original domain by utilizing the input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution;
inputting all wind speed values and all load values into the deterministic load flow calculation network to obtain updated load flow calculation results;
comparing the initial load flow calculation result with the updated load flow calculation result, and judging whether a preset convergence condition is reached according to the obtained comparison result;
if not, repeatedly executing the step of generating a preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing the ILHS algorithm until the convergence condition is reached;
and if so, monitoring the running state of the power grid according to the updated load flow calculation result.
In an embodiment of the present invention, after obtaining an initial number of sample points in a uniform distribution, before transforming each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm, the method further includes:
sequencing each sample point by using a cholesky decomposition algorithm to obtain an initial sequencing result;
transforming each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm, comprising:
transforming each sample point in the sample point matrix to the original domain by utilizing the input probability model established based on the NPNT algorithm according to the initial sequencing result;
after generating a preset number of sample points in the unrepresented probability space of the sample point matrix by using the ILHS algorithm, before transforming each of the newly generated sample points to the original domain by using the input probability model, the method further includes:
reordering all the sample points by using a cholesky decomposition algorithm to obtain an updated ordering result;
transforming each of the newly generated sample points to the original domain using the input probability model, including:
and transforming each newly generated sample point to the original domain by using the input probability model according to the updated sequencing result.
In one embodiment of the present invention, transforming each sample point in the sample point matrix to the original domain by using the input probability model established based on NPNT algorithm includes:
and transforming each sample point in the sample point matrix to the original domain by utilizing a nine-order polynomial input probability model established based on an NPNT algorithm.
In an embodiment of the present invention, after completing monitoring the operating state of the power grid according to the updated load flow calculation result, the method further includes:
acquiring a monitoring result of the running state of the power grid;
and evaluating the effectiveness of the monitoring result of the running state of the power grid.
A grid operating condition monitoring device, comprising:
the system comprises an initial value acquisition module, a load calculation module and a load calculation module, wherein the initial value acquisition module is used for acquiring an initial number of sample points in uniform distribution to obtain a sample point matrix when a power grid running state monitoring instruction is received, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm to obtain each initial wind speed value and each initial load value which obey any distribution; wherein the input probability model is established based on wind speed historical data and load historical data;
the initial result acquisition module is used for inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result;
the new added value acquisition module is used for generating a preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and transforming the newly generated sample points to the original domain by utilizing the input probability model to obtain each newly added wind speed value and each newly added load value which obey any distribution;
the updated result acquisition module is used for inputting all wind speed values and all load values into the deterministic load flow calculation network to obtain an updated load flow calculation result;
the judging module is used for comparing the initial load flow calculation result with the updated load flow calculation result, judging whether a preset convergence condition is reached according to the obtained comparison result, if not, triggering the new value-added acquisition module, and if so, triggering the state monitoring module;
and the state monitoring module is used for monitoring the running state of the power grid according to the updated load flow calculation result.
In one embodiment of the present invention, the method further comprises:
a sorting result obtaining module, configured to, after obtaining an initial number of sample points in uniform distribution, transform each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm, and sort each sample point by using a cholesky decomposition algorithm to obtain an initial sorting result; after a preset number of sample points are generated in an unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, before each newly generated sample point is transformed to the original domain by utilizing the input probability model, all the sample points are reordered by utilizing a cholesky decomposition algorithm to obtain an updated ordering result;
the new value-added acquisition module comprises a domain transformation submodule, wherein the domain transformation submodule is a module which transforms each sample point in the sample point matrix to the original domain by using the input probability model established based on the NPNT algorithm according to the initial sequencing result and transforms each newly generated sample point to the original domain by using the input probability model according to the updated sequencing result.
In an embodiment of the present invention, the domain transformation sub-module is a module that transforms each sample point in the sample point matrix to the original domain by using a nine-order polynomial input probability model established based on the NPNT algorithm.
In one embodiment of the present invention, the method further comprises:
the monitoring result obtaining module is used for obtaining a monitoring result of the running state of the power grid after the monitoring of the running state of the power grid is completed according to the updated load flow calculation result;
and the effectiveness evaluation module is used for evaluating the effectiveness of the monitoring result of the running state of the power grid.
A grid operating condition monitoring device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the grid operation state monitoring method as described above when executing the computer program.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the grid operating condition monitoring method as set forth above.
The invention provides a method for monitoring the running state of a power grid, which comprises the following steps: when a power grid operation state monitoring instruction is received, acquiring an initial number of sample points in uniform distribution to obtain a sample point matrix, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established by an NPNT algorithm to obtain each initial wind speed value and each initial load value obeying arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data; inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result; generating a preset number of sample points in an unrepresented probability space of a sample point matrix by utilizing an ILHS algorithm, and transforming each newly generated sample point to an original domain by utilizing an input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution; inputting all wind speed values and all load values into a deterministic load flow calculation network to obtain updated load flow calculation results; comparing the initial load flow calculation result with the updated load flow calculation result, and judging whether a preset convergence condition is reached according to the obtained comparison result; if not, repeatedly executing the step of generating the preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing the ILHS algorithm until a convergence condition is reached; and if so, monitoring the running state of the power grid according to the updated load flow calculation result.
According to the technical scheme, the acquired sample points are converted to the original domain by using the input probability model established based on the wind speed historical data and the load historical data through the NPNT algorithm, so that the wind speed value and the load value which are subjected to random distribution are obtained, the input probability model can be established directly based on the wind speed historical data and the load historical data without acquiring a probability density function in advance, the power grid probability power flow analysis precision is greatly improved, the number of the sample points required by reaching the preset convergence condition can be self-adaptively estimated through iterative calculation by using the ILHS algorithm, the power grid probability power flow analysis efficiency is greatly improved, the balance of the power grid probability power flow analysis precision and efficiency is realized, and the effective monitoring of the running state of the power grid is realized.
Correspondingly, the embodiment of the invention also provides a power grid running state monitoring device, equipment and a computer readable storage medium corresponding to the power grid running state monitoring method, which have the technical effects and are not described herein again.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of an implementation of a method for monitoring an operating state of a power grid according to an embodiment of the present invention;
fig. 2 is a flowchart of another implementation of the method for monitoring the operating state of the power grid according to the embodiment of the present invention;
FIG. 3(a) is a distribution diagram of sample points generated initially in an embodiment of the present invention;
FIG. 3(b) is a schematic diagram of non-overlapping regions divided according to an embodiment of the present invention;
FIG. 3(c) is a diagram illustrating the remaining intervals after deleting the represented intervals in the sample point matrix according to the embodiment of the present invention;
FIG. 3(d) is a schematic diagram of generating new sample points in the remaining intervals after deleting the intervals already represented in the sample point matrix according to the embodiment of the present invention;
FIG. 3(e) is a schematic diagram illustrating the remaining interval after the new sample point is generated being returned to the sample matrix according to the embodiment of the present invention;
fig. 4 is a schematic diagram of a probability trend algorithm adopted in the embodiment of the present invention and a convergence trend of an existing probability trend algorithm;
fig. 5 is a schematic diagram of a time length used for iterative computation by using a probability load flow algorithm and an existing probability load flow algorithm according to an embodiment of the present invention;
fig. 6 is a block diagram of a power grid operation state monitoring apparatus according to an embodiment of the present invention;
fig. 7 is a block diagram of a power grid operation state monitoring device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a flowchart of an implementation of a method for monitoring an operating state of a power grid according to an embodiment of the present invention, where the method may include the following steps:
s101: when a power grid operation state monitoring instruction is received, an initial number of sample points in uniform distribution are obtained to obtain a sample point matrix, each sample point in the sample point matrix is converted to an original domain by using an input probability model established based on an NPNT algorithm, and each initial wind speed value and each initial load value obeying random distribution are obtained.
Wherein the input probability model is established based on wind speed historical data and load historical data.
When the power grid operation state needs to be monitored, a power grid operation state monitoring instruction can be sent to a power grid operation state monitoring center, the power grid operation state monitoring center receives the power grid operation state monitoring instruction, an initial number of sample points are obtained in uniform distribution, and a sample point matrix is obtained. The input probability model can be established in advance based on the NPNT algorithm according to the wind speed historical data and the load historical data, and the process of establishing the input probability model can comprise estimating a ninth-order polynomial coefficient according to the wind speed historical data and the load historical data, and calculating a correlation matrix of a standard Gaussian domain according to the ninth-order polynomial corresponding to the obtained ninth-order polynomial coefficient. After the input probability model is established, each sample point in the sample point matrix is transformed to an original domain by using the input probability model, and the specific process can include transforming the initial number of acquired sample points on the uniform distribution to a standard gaussian distribution domain, calculating the sample points on the standard gaussian distribution based on the obtained correlation matrix of the standard gaussian domain, and transforming the sample points on the gaussian distribution to the original domain based on a ninth-order polynomial, so as to obtain each initial wind speed value and each initial load value which obey any distribution.
It should be noted that the initial number may be set and adjusted according to actual situations, which is not limited in the embodiment of the present invention.
S102: and inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result.
After obtaining the initial wind speed values and the initial load values, the initial wind speed values and the initial load values may be input into a deterministic load flow calculation network, for example, for an AC/VSC-MTDC hybrid grid, the initial wind speed values and the initial load values may be input into the deterministic load flow calculation network to obtain an initial load flow calculation result.
S103: and generating a preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and transforming each newly generated sample point to an original domain by utilizing an input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution.
After a sample point matrix formed by the initial number of sample points is obtained, a preset number of sample points can be generated in an unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and each newly generated sample point is converted to an original domain by utilizing an input probability model, so that each newly added wind speed value and each newly added load value which obey any distribution are obtained.
The core idea of the ILHS algorithm is that a small number of sample points are selected to perform probability load flow analysis, and then the sample points are gradually increased according to the calculation precision requirement and the convergence condition. A new sample point is added to the sample point matrix made up of the initial number of sample points. The advantage of directly generating a new sample point set is that the calculation result obtained by the existing sample points can be reused, thereby improving the calculation efficiency. This is significant for probabilistic power flow calculations, especially for deterministic ac/dc hybrid grid power flow calculations, which are more complex and time consuming than pure ac power grids. Based on the convergence accuracy of the given probability power flow analysis, the ILHS algorithm can adaptively evaluate the required sample size, so that the calculation efficiency is greatly improved. In addition, the ILHS algorithm can also obtain data moment information (such as mean and standard deviation) of the probabilistic load flow analysis, a PDF function and the like.
To further illustrate the ILHS algorithm, two random variables uma, umb subject to uniform distribution may be defined with reference to fig. 3(a) to 3 (e). Assume that 3 sample points are initially generated as shown in fig. 3 (a). The ILHS algorithm aims to continue to add new sample points in the already generated sample point matrix (fig. 3(a)) and to form a new set of sample points. Assume that a total of 6 sample points need to be generated. That is, 3 additional sample points are required in fig. 3 (a). The random variables uma, umb may be equally divided into 6 non-overlapping intervals as shown in fig. 3 (b). In fig. 3(b), the space represented by the already generated sample points is covered by the stripe portion. As shown in fig. 3(c), if the space represented in fig. 3(b) is deleted, the remaining unrepresentative space (empty space) appears.
As shown in fig. 3(d), the location of three sample points in white space is easily determined based on the idea of the conventional latin hypercube sampling technique CLHS. As shown in fig. 3(e), when the blank space is put back into the uniform distribution, the inherited sample points (dots) and the new added sample points (squares) form a new sample point set. The new group of sample points (6 groups of sample points) can more comprehensively cover the probability distribution of random variables than the original sample point set (3 groups of sample points). After obtaining the new sample points, the new sample points may be transformed into the original probability distribution space and then input into the deterministic load flow calculation network. It is worth noting that the power grid probabilistic power flow analysis based on the ILHS algorithm can reuse the obtained sample points and the probabilistic power flow results thereof, thereby greatly improving the calculation accuracy and efficiency.
It should be noted that the preset number may be set and adjusted according to an actual situation, which is not limited in the embodiment of the present invention, for example, the preset number may be set to be relatively larger when the preset convergence accuracy is higher, and may be set to be relatively smaller when the preset convergence accuracy is lower.
S104: and inputting all the wind speed values and all the load values into a deterministic load flow calculation network to obtain an updated load flow calculation result.
After each newly added wind speed value and each newly added load value which obey any distribution are obtained, all the wind speed values and all the load values can be input into a deterministic load flow calculation network, and probability load flow calculation is carried out again based on the initial wind speed value, the initial load value, the newly added wind speed value and the newly added load value to obtain an updated load flow calculation result.
S105: and comparing the initial load flow calculation result with the updated load flow calculation result, judging whether a preset convergence condition is reached according to the obtained comparison result, if not, executing the step S103, and if so, executing the step S106.
A convergence condition may be preset, after an updated power flow calculation result is obtained, an initial power flow calculation result may be compared with the updated power flow calculation result, whether a preset convergence condition is reached is determined according to the obtained comparison result, if not, it is indicated that the difference between the power flow calculation results obtained at the previous time and the power flow calculation results obtained at the next time is still relatively large, the step of generating a preset number of sample points in the unrepresented probability space of the sample point matrix by using the ILHS algorithm in step S103 may be repeatedly performed, and if so, it is indicated that the difference between the power flow calculation results obtained at the previous time and the power flow calculation results obtained at the next time is relatively small, and step S106 may be continuously performed.
The convergence condition and the convergence accuracy may be set as follows:
the convergence accuracy β is set to 5% in advance;
the convergence conditions are as follows:
wherein the content of the first and second substances,
and data moment information of a probability load flow calculation result after the kth iteration.
Any one of the first to ninth moments information may be selected as the convergence condition.
S106: and completing monitoring of the running state of the power grid according to the updated load flow calculation result.
And after the preset convergence condition is determined to be reached, monitoring the running state of the power grid can be completed according to the updated load flow calculation result.
According to the technical scheme, the acquired sample points are converted to the original domain by using the input probability model established based on the wind speed historical data and the load historical data through the NPNT algorithm, so that the wind speed value and the load value which are subjected to random distribution are obtained, the input probability model can be established directly based on the wind speed historical data and the load historical data without acquiring a probability density function in advance, the power grid probability power flow analysis precision is greatly improved, the number of the sample points required by reaching the preset convergence condition can be self-adaptively estimated through iterative calculation by using the ILHS algorithm, the power grid probability power flow analysis efficiency is greatly improved, the balance of the power grid probability power flow analysis precision and efficiency is realized, and the effective monitoring of the running state of the power grid is realized.
It should be noted that, based on the first embodiment, the embodiment of the present invention further provides a corresponding improvement scheme. In the following embodiments, steps that are the same as or correspond to those in the first embodiment may be referred to each other, and corresponding advantageous effects may also be referred to each other, which are not described in detail in the following modified embodiments.
Example two:
referring to fig. 2, fig. 2 is a flowchart of another implementation of the method for monitoring the operating state of the power grid according to the embodiment of the present invention, where the method may include the following steps:
s201: and when a power grid running state monitoring instruction is received, obtaining an initial number of sample points in uniform distribution to obtain a sample point matrix.
S202: and sequencing each sample point by using a cholesky decomposition algorithm to obtain an initial sequencing result.
After a sample point matrix composed of an initial number of sample points is obtained, the sample points may be sorted by using a cholesky decomposition algorithm to obtain an initial sorting result.
S203: and transforming each sample point in the sample point matrix to an original domain by utilizing a nine-order polynomial input probability model established based on an NPNT algorithm according to the initial sequencing result to obtain each initial wind speed value and each initial load value which obey arbitrary distribution.
Wherein the input probability model is established based on wind speed historical data and load historical data.
After the initial sorting result is obtained, each sample point in the sample point matrix can be transformed to an original domain by using a nine-order polynomial input probability model established based on an NPNT algorithm according to the initial sorting result, and the specific process can comprise transforming the initial number of the obtained sample points on uniform distribution to an independent Gaussian distribution domain, and using Z to obtain a Z sample pointindependentExpressing random variables subject to independent standard Gaussian distributions, and pre-determining a correlation matrix R between the Gaussian distributionsZWherein the correlation matrix can be represented as:
Rz＝LLΤ；
wherein L represents a lower triangular matrix obtained by sorting each sample point by Cholesky decomposition.
By the formula:
Z＝LZindependent；
can calculate the correlation as RZThe multi-dimensional standard normal distribution variable Z. And substituting the multidimensional normal distribution variable Z containing the correlation into a corresponding ninth-order polynomial:
wherein, ai,kIs a polynomial coefficient.
To obtain correlated and normalized randomly distributed random variables. And (4) de-normalizing the normalized random variables to obtain the multidimensional random variables which have correlation and are subject to random distribution.
In the case of an input probability model established by the NPNT algorithm, the polynomial coefficient estimation process may include:
the ninth order polynomial may be expressed as:
wherein x isoRepresenting a continuous type random variable (e.g. wind speed), mu, in an actual gridxAnd σxTo represent the input random variable xoX represents the input random variable after normalization. z represents a random variable following a standard normal distribution, ak(k 1, 2, 9), random variables subject to an arbitrary distribution will be modeled by random variables that are subject to a standard normal distribution. Data moments are typically used to characterize the probability features of random data. The Probability Weight Moment (PWM) is adopted to describe the probability characteristics of the historical data of the random source in the power grid. The method for calculating PWM is as follows:
sorting input random variables by size x1≤…≤xi…≤xnThen, PWM can be obtained by the following equation:
the coefficients of the ninth order polynomial can be found based on:
wherein Φ (z) and
respectively, a cumulative probability distribution function and a probability density function representing a standard normal distribution.
Denotes a constant value which can be determined by numerical integration. Based on the above linearized equation, the coefficients of the ninth order polynomial can be easily solved.
The estimation process of the correlation coefficient of the standard normal space may include:
the correlation between random variables in the power grid of adjacent regions cannot be ignored. Let x be1And x2Are two random variables that are subject to arbitrary distribution and normalization. From the above nine-order polynomial, x is simulated by a standard normal distribution1And x2It can be expressed as:
random variable z1、z2Correlation coefficient ρ between (following a standard normal distribution)zAnd a random variable x1、x2(possibly following an arbitrary distribution) correlation coefficient p between themxThe functional relationship of (a) can be expressed as:
wherein, murAnd σrRespectively, mean and standard deviation of random variables subject to arbitrary distribution.
E(x1x2) May be expressed in relation to pzThe above equation can be written as:
in general, the random variable x can be estimated from historical data1、x2(possibly following an arbitrary distribution) correlation coefficient p between themxThe random variable z can be obtained from the above formula by using the dichotomy1、z2Correlation coefficient ρ between (following a standard normal distribution)z。
A correlation coefficient matrix obtained based on random variable historical data:
correlation coefficient matrix capable of correspondingly solving standard normal space
Because the wind speed value and the load value are data with correlation, the accuracy of the power grid probability power flow analysis can be further improved by converting the independent Gaussian distribution into the Gaussian distribution random variable with the correlation.
S204: and inputting each wind speed value and each load value into a deterministic load flow calculation network to obtain an initial load flow calculation result.
S205: a preset number of sample points are generated in the unrepresented probability space of the sample point matrix using the ILHS algorithm.
S206: and reordering all sample points by using a cholesky decomposition algorithm to obtain an updated ordering result.
After a preset number of sample points are generated in the unrepresented probability space of the sample point matrix, all the sample points can be reordered by using a cholesky decomposition algorithm to obtain an updated ordering result.
S207: and according to the updated sequencing result, transforming each newly generated sample point to an original domain by using an input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution.
After the updated sequencing result is obtained, each newly generated sample point can be converted to the original domain by using the input probability model, and each newly added wind speed value and each newly added load value which obey arbitrary distribution are obtained. For the process of converting each newly generated sample point into the original domain, reference may be made to the relevant description in step S203, which is not described herein for further details.
S208: and inputting all the wind speed values and all the load values into a deterministic load flow calculation network to obtain an updated load flow calculation result.
S209: and comparing the initial load flow calculation result with the updated load flow calculation result, judging whether a preset convergence condition is reached according to the obtained comparison result, if not, executing the step S205, and if so, executing the step S210.
S210: and completing monitoring of the running state of the power grid according to the updated load flow calculation result.
S211: and acquiring a monitoring result of the running state of the power grid.
And monitoring the running state of the power grid is completed according to the updated load flow calculation result, and a monitoring result of the running state of the power grid is obtained.
S212: and evaluating the effectiveness of the monitoring result of the running state of the power grid.
After the monitoring result of the power grid running state is obtained, validity evaluation can be performed on the monitoring result of the power grid running state.
Referring to FIG. 4, FIG. 4 shows the average value of the DC bus voltage FHSI that results when the NPNT-ILHS and NPNT-CLHS algorithms use different sample sizes. Obviously, the calculation accuracy of the NPNT-ILHS and NPNT-CLHS algorithms is almost the same. The average values of the NPNT-CLHS and NPNT-ILHS algorithm DC bus voltage FHSI were 94.32%, 92.15%, and 94.07% (NPNT-CLHS), 94.09%, 92.47%, and 93.89% (NPNT-ILHS), respectively, when the sample sizes were 3600, 3700, and 3800. From the numerical results, it can be seen that the actuations of the two algorithms are very close.
Referring to FIG. 5, the calculation time of the NPNT-CLHS and NPNT-ILHS algorithms increases as the sample volume increases from 100 to 3800 (100 per increment). The calculation times for the NPNT-CLHS and NPNT-ILHS algorithms were 21.71s,42.36s,65.98s,85.75s and 103.89s (for the NPNT-CLHS algorithm), 21.75s,21.91s,22.05s,22.24s and 22.51s (for the NPNT-ILHS algorithm), respectively, when the sample volumes were 100, 200, 300, 400 and 500, respectively. As can be seen from FIG. 5, the NPNT-ILHS algorithm has a computation time of less than 40s during each iteration, whereas the NPNT-CLHS algorithm has a computation time of more than 40s during most iterations. Obviously, the probabilistic power flow algorithm provided by the embodiment of the invention can greatly improve the calculation efficiency of the alternating current-direct current hybrid power grid. The NPNT-ILHS algorithm can newly add sample points in the existing sample point set and reuse the obtained probability load flow calculation results, so that the efficiency of the NPNT-ILHS algorithm in probability analysis is greatly improved. The NPNT-CLHS algorithm does not have the capability of evaluating the sample capacity required by the probabilistic power flow analysis of the alternating-current and direct-current hybrid power grid, so that a brand-new sample point set needs to be generated again and the probabilistic power flow analysis is carried out on the power grid again during each calculation, and the calculation burden is increased.
Corresponding to the above method embodiment, the embodiment of the present invention further provides a power grid operation state monitoring device, and the power grid operation state monitoring device described below and the power grid operation state monitoring method described above may be referred to in a corresponding manner.
Referring to fig. 6, fig. 6 is a block diagram of a structure of a device for monitoring an operating state of a power grid according to an embodiment of the present invention, where the device may include:
the initial value acquisition module 61 is used for acquiring an initial number of sample points in uniform distribution to obtain a sample point matrix when receiving a power grid operation state monitoring instruction, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established based on an NPNT algorithm to obtain each initial wind speed value and each initial load value obeying arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data;
an initial result obtaining module 62, configured to input each initial wind speed value and each initial load value into a deterministic load flow calculation network, so as to obtain an initial load flow calculation result;
a new added value obtaining module 63, configured to generate a preset number of sample points in an unrepresented probability space of the sample point matrix by using an ILHS algorithm, and transform each newly generated sample point to an original domain by using an input probability model to obtain each new wind speed value and each new load value that obey arbitrary distribution;
an updated result obtaining module 64, configured to input all wind speed values and all load values into the deterministic load flow calculation network to obtain an updated load flow calculation result;
the judging module 65 is configured to compare the initial power flow calculation result with the updated power flow calculation result, judge whether a preset convergence condition is met according to the obtained comparison result, if not, trigger the new value-added obtaining module, and if so, trigger the state monitoring module;
and the state monitoring module 66 is used for completing monitoring of the operation state of the power grid according to the updated load flow calculation result.
According to the technical scheme, the acquired sample points are converted to the original domain by using the input probability model established based on the wind speed historical data and the load historical data through the NPNT algorithm, so that the wind speed value and the load value which are subjected to random distribution are obtained, the input probability model can be established directly based on the wind speed historical data and the load historical data without acquiring a probability density function in advance, the power grid probability power flow analysis precision is greatly improved, the number of the sample points required by reaching the preset condition can be self-adaptively estimated through iterative calculation by using the ILHS algorithm, the power grid probability power flow analysis efficiency is greatly improved, the balance of the power grid probability power flow analysis precision and efficiency is realized, and the effective monitoring of the running state of the power grid is realized.
In one embodiment of the present invention, the apparatus may further include:
the sequencing result obtaining module is used for utilizing an NPNT algorithm to establish an input probability model after obtaining an initial number of sample points in uniform distribution, and utilizing a cholesky decomposition algorithm to sequence the sample points before transforming the sample points in a sample point matrix to an original domain to obtain an initial sequencing result; after a preset number of sample points are generated in an unrepresented probability space of a sample point matrix, all newly generated sample points are reordered by using a cholesky decomposition algorithm before transforming the newly generated sample points to an original domain by using an input probability model, and an updated ordering result is obtained;
the new value-added acquisition module 63 includes a domain transformation submodule, which is specifically a module that transforms each sample point in the sample point matrix to the original domain by using an input probability model established based on the NPNT algorithm according to the initial sequencing result, and transforms each newly generated sample point to the original domain by using the input probability model according to the updated sequencing result.
In an embodiment of the present invention, the domain transformation sub-module is a module that transforms each sample point in the sample point matrix to the original domain by using a nine-order polynomial input probability model established based on the NPNT algorithm.
In one embodiment of the present invention, the apparatus may further include:
the monitoring result obtaining module is used for obtaining a monitoring result of the running state of the power grid after the monitoring of the running state of the power grid is completed according to the updated load flow calculation result;
and the effectiveness evaluation module is used for evaluating the effectiveness of the monitoring result of the running state of the power grid.
Corresponding to the above method embodiment, referring to fig. 7, fig. 7 is a schematic diagram of a device for monitoring an operation state of a power grid provided by the present invention, where the device may include:
a memory 71 for storing a computer program;
the processor 72, when executing the computer program stored in the memory 71, may implement the following steps:
when a power grid operation state monitoring instruction is received, acquiring an initial number of sample points in uniform distribution to obtain a sample point matrix, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established by an NPNT algorithm to obtain each initial wind speed value and each initial load value obeying arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data; inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result; generating a preset number of sample points in an unrepresented probability space of a sample point matrix, and transforming each newly generated sample point to an original domain by using an input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution; inputting all wind speed values and all load values into a deterministic load flow calculation network by utilizing an ILHS algorithm to obtain an updated load flow calculation result; comparing the initial load flow calculation result with the updated load flow calculation result, and judging whether a preset convergence condition is reached according to the obtained comparison result; if not, repeatedly executing the step of generating the preset number of sample points in the unrepresented probability space of the sample point matrix until a convergence condition is reached; and if so, monitoring the running state of the power grid according to the updated load flow calculation result.
For the introduction of the device provided by the present invention, please refer to the above method embodiment, which is not described herein again.
Corresponding to the above method embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of:
when a power grid operation state monitoring instruction is received, acquiring an initial number of sample points in uniform distribution to obtain a sample point matrix, and transforming each sample point in the sample point matrix to an original domain by using an input probability model established by an NPNT algorithm to obtain each initial wind speed value and each initial load value obeying arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data; inputting each wind speed value and each load value into a deterministic load flow calculation network to obtain an initial load flow calculation result; generating a preset number of sample points in an unrepresented probability space of a sample point matrix, and transforming each newly generated sample point to an original domain by using an input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution; inputting all wind speed values and all load values into a deterministic load flow calculation network by utilizing an ILHS algorithm to obtain an updated load flow calculation result; comparing the initial load flow calculation result with the updated load flow calculation result, and judging whether a preset convergence condition is reached according to the obtained comparison result; if not, repeatedly executing the step of generating the preset number of sample points in the unrepresented probability space of the sample point matrix until a convergence condition is reached; and if so, monitoring the running state of the power grid according to the updated load flow calculation result.
The computer-readable storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
For the introduction of the computer-readable storage medium provided by the present invention, please refer to the above method embodiments, which are not described herein again.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device, the apparatus and the computer-readable storage medium disclosed in the embodiments correspond to the method disclosed in the embodiments, so that the description is simple, and the relevant points can be referred to the description of the method.
The principle and the implementation of the present invention are explained in the present application by using specific examples, and the above description of the embodiments is only used to help understanding the technical solution and the core idea of the present invention. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (4)
1. A method for monitoring the operation state of a power grid is characterized by comprising the following steps:
when a power grid operation state monitoring instruction is received, obtaining an initial number of sample points in uniform distribution to obtain a sample point matrix, and sequencing the sample points by using a cholesky decomposition algorithm to obtain an initial sequencing result; converting each sample point in the sample point matrix to an original domain by utilizing a nine-order polynomial input probability model established based on an NPNT algorithm according to the initial sequencing result to obtain each initial wind speed value and each initial load value which obey arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data;
inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result;
generating a preset number of sample points in an unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and reordering all the sample points by utilizing a cholesky decomposition algorithm to obtain an updated ordering result; according to the updated sequencing result, transforming the newly generated sample points to the original domain by using the input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution; the process of generating the sample points by using the ILHS algorithm comprises the steps of firstly selecting a small number of sample points to perform probability power flow analysis, then gradually increasing the sample points according to the calculation precision requirement and the convergence condition, and adding new sample points in a sample point matrix formed by the initial number of sample points;
inputting all wind speed values and all load values into the deterministic load flow calculation network to obtain updated load flow calculation results;
comparing the initial load flow calculation result with the updated load flow calculation result, and judging whether a preset convergence condition is reached according to the obtained comparison result;
if not, repeatedly executing the step of generating a preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing the ILHS algorithm until the convergence condition is reached;
if so, monitoring the running state of the power grid according to the updated load flow calculation result;
acquiring a monitoring result of the running state of the power grid;
and evaluating the effectiveness of the monitoring result of the running state of the power grid.
2. A grid operating condition monitoring device, comprising:
the system comprises an initial value acquisition module, a data processing module and a data processing module, wherein the initial value acquisition module is used for acquiring an initial number of sample points in uniform distribution when a power grid running state monitoring instruction is received to obtain a sample point matrix, and sequencing the sample points by using a cholesky decomposition algorithm to obtain an initial sequencing result; converting each sample point in the sample point matrix to an original domain by utilizing a nine-order polynomial input probability model established based on an NPNT algorithm according to the initial sequencing result to obtain each initial wind speed value and each initial load value which obey arbitrary distribution; wherein the input probability model is established based on wind speed historical data and load historical data;
the initial result acquisition module is used for inputting each initial wind speed value and each initial load value into a deterministic load flow calculation network to obtain an initial load flow calculation result;
the new value-added acquisition module is used for generating a preset number of sample points in the unrepresented probability space of the sample point matrix by utilizing an ILHS algorithm, and reordering all the sample points by utilizing a cholesky decomposition algorithm to obtain an updated ordering result; according to the updated sequencing result, transforming the newly generated sample points to the original domain by using the input probability model to obtain each newly added wind speed value and each newly added load value which obey arbitrary distribution; the process of generating the sample points by using the ILHS algorithm comprises the steps of firstly selecting a small number of sample points to perform probability power flow analysis, then gradually increasing the sample points according to the calculation precision requirement and the convergence condition, and adding new sample points in a sample point matrix formed by the initial number of sample points;
the updated result acquisition module is used for inputting all wind speed values and all load values into the deterministic load flow calculation network to obtain an updated load flow calculation result;
the judging module is used for comparing the initial load flow calculation result with the updated load flow calculation result, judging whether a preset convergence condition is reached according to the obtained comparison result, if not, triggering the new value-added acquisition module, and if so, triggering the state monitoring module;
the state monitoring module is used for monitoring the running state of the power grid according to the updated load flow calculation result; acquiring a monitoring result of the running state of the power grid; and evaluating the effectiveness of the monitoring result of the running state of the power grid.
3. An electrical grid operating condition monitoring device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the grid operating condition monitoring method according to claim 1 when executing the computer program.
4. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for monitoring an operational status of an electrical grid according to claim 1.
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