CN113723821B - Power grid fault early warning method and device based on tide betweenness - Google Patents

Power grid fault early warning method and device based on tide betweenness Download PDF

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CN113723821B
CN113723821B CN202111017116.2A CN202111017116A CN113723821B CN 113723821 B CN113723821 B CN 113723821B CN 202111017116 A CN202111017116 A CN 202111017116A CN 113723821 B CN113723821 B CN 113723821B
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侯祖锋
赵瑞锋
林桂辉
李波
陈建钿
王超
卢建刚
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Guangdong Power Grid Co Ltd
Zhuhai Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The application discloses a power grid fault early warning method and device based on tide betweenness, wherein the method comprises the following steps: solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant; expressing the state high-order semi-invariant into a power flow state probability density function, and constructing a branch equivalent pi-type parameter model; constructing an active and reactive downstream distribution matrix based on the load flow state probability density function and the branch equivalent pi-type parameter model; calculating corresponding transmission active power and transmission reactive power according to the active and reactive downstream distribution matrixes respectively; respectively solving a line load flow medium number and a node load flow medium number according to the transmission active power and the transmission reactive power; and calculating a severity value of the fault result according to the line load flow betweenness and the node load flow betweenness according to a preset membership function to obtain a fault early warning grade. The application can solve the technical problems of error and lower accuracy of the early warning result in the prior art.

Description

Power grid fault early warning method and device based on tide betweenness
Technical Field
The application relates to the technical field of power grid fault detection, in particular to a power grid fault early warning method and device based on tide betweenness.
Background
Along with the increasing access of new energy sources such as wind power generation, photovoltaic power generation and the like to the power grid, the safety and the stability of the power grid are affected to a certain extent by the operation mode of the interconnection of the power grids among all areas. Meanwhile, considering random changes of uncertain factors such as loads, wind power output and the like in a system, the traditional grid fault early warning method based on deterministic power flow calculation cannot adapt to the randomness of a distributed power supply.
In the existing method, a fault early warning scheme adopting a deterministic evaluation method and a fault early warning scheme adopting a probabilistic evaluation method are mainly available. Firstly, adopting a deterministic evaluation method to consider early warning evaluation of indexes of a certain aspect of a system, such as system load capacity, voltage situation and the like, and mainly researching the change of the grade to which the indexes belong under different operation modes of a power grid; secondly, with the development of interdisciplinary subjects such as mathematics, a probabilistic method for overcoming the defects of a deterministic method is generated, and the sufficiency and the stability of the power system are comprehensively evaluated by adopting the probabilistic method, so that comprehensive indexes are given to early warn the fault condition.
In the prior art, the reliability of the power system is analyzed and evaluated by adopting N-1 criteria in a deterministic method, and the method considers the worst result of the system, so that the evaluation result is too conservative and is not suitable for quantitatively analyzing the reliability of the system; probability assessment, while overcoming the drawbacks of deterministic assessment, lacks quantitative analysis of fault results. Therefore, the distributed power supply is largely connected into the power grid, the early warning model established by the existing method is incomplete, and consideration of random characteristics of the distributed power supply is absent. If the method based on deterministic power flow calculation is unsuitable in analyzing the randomness of the distributed power supply, the final calculation result is inaccurate, and the fault early warning is easy to deviate. The probability-based method cannot express the fuzzy factors by specific data, so that a certain error occurs in the early warning result.
Disclosure of Invention
The application provides a power grid fault early warning method and device based on tide betters, which are used for solving the technical problems that the existing deterministic evaluation and probabilistic evaluation methods are either too conservative or lack quantitative analysis, so that the actual early warning result has errors and the accuracy is lower.
In view of the foregoing, a first aspect of the present application provides a power grid fault early warning method based on tide betters, including:
solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant;
Expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function, and constructing a branch equivalent pi-type parameter model;
constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model;
calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively;
respectively solving a line load flow betweenness and a node load flow betweenness according to the transmission active power and the transmission reactive power;
and calculating a fault result severity value through the line load flow betweenness and the node load flow betweenness, and then calculating a fault early warning grade according to the fault result severity value according to a preset membership function.
Optionally, the solving the node power change vector calculated according to the preset node power equation based on the semi-invariant method to obtain a state high-order semi-invariant includes:
Expressing a preset node power equation into a matrix form to obtain a node power vector;
linearizing the node power vector by adopting a Taylor series expression mode to obtain a node power change vector;
and solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
Optionally, the calculating the corresponding transmission active power and the corresponding transmission reactive power according to the active downstream distribution matrix and the reactive downstream distribution matrix respectively includes:
Calculating an active distribution matrix and an active drawing matrix between the motor and the load according to the active forward distribution matrix;
calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix;
Calculating a reactive power distribution matrix and a reactive power drawing matrix between the motor and the load according to the reactive power forward distribution matrix;
and calculating the transmission reactive power between the motor and the load based on the reactive power distribution matrix and the reactive power drawing matrix.
Optionally, after calculating the severity value of the fault result according to the line load flow betweenness and the node load flow betweenness, calculating a fault early warning level according to the severity value of the fault result according to a preset membership function, including:
calculating an overload severity index value of the line and an out-of-limit severity index value of the node voltage respectively through the line load flow betweenness and the node load flow betweenness;
Solving the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value;
calculating a fault early warning grade according to a preset membership function according to the fault result severity value, wherein the preset membership function is as follows:
wherein x is the severity value of the fault result, a, b, c, d is a preset level boundary value.
Optionally, after calculating the severity value of the fault result according to the line load flow betweenness and the node load flow betweenness, calculating a fault early warning level according to the severity value of the fault result according to a preset membership function, and then further including:
And providing different early warning prompts according to the fault early warning level, wherein the early warning prompts comprise critical, serious, attention and normal.
The second aspect of the application provides a power grid fault early warning device based on tide betweenness, which comprises:
the first calculation module is used for solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant;
The model construction module is used for expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function and then constructing a branch equivalent pi-type parameter model;
the matrix construction module is used for constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model;
The second calculation module is used for calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively;
the medium number obtaining module is used for respectively obtaining line load flow medium numbers and node load flow medium numbers according to the transmission active power and the transmission reactive power;
And the grade early warning module is used for calculating the fault result severity value according to the preset membership function after calculating the fault result severity value through the line load flow betweenness and the node load flow betweenness, and calculating the fault early warning grade according to the fault result severity value.
Optionally, the first computing module includes:
The expression conversion sub-module is used for expressing a preset node power equation into a matrix form to obtain a node power vector;
the linear processing sub-module is used for carrying out linearization processing on the node power vector by adopting a Taylor series expression mode to obtain a node power change vector;
and the vector solving sub-module is used for solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
Optionally, the second computing module includes:
the active matrix calculation sub-module is used for calculating an active distribution matrix and an active drawing matrix between the motor and the load according to the active downstream distribution matrix;
An active power calculation sub-module for calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix;
the reactive matrix calculation sub-module is used for calculating a reactive distribution matrix and a reactive drawing matrix between the motor and the load according to the reactive downstream distribution matrix;
and the reactive power calculation sub-module is used for calculating the transmission reactive power between the motor and the load based on the reactive power distribution matrix and the reactive power drawing matrix.
Optionally, the level early warning module includes:
the primary severity calculation sub-module is used for calculating a line overload severity index value and a node voltage out-of-limit severity index value through the line load flow betweenness and the node load flow betweenness respectively;
the fault severity computing sub-module is used for obtaining the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value;
The fault grade dividing sub-module is used for calculating a fault early warning grade according to a preset membership function according to the fault result severity value, wherein the preset membership function is as follows:
wherein x is the severity value of the fault result, a, b, c, d is a preset level boundary value.
Optionally, the method further comprises:
and the early warning prompt module is used for providing different early warning prompts according to the fault early warning level, and the early warning prompts comprise critical, serious, attention and normal.
From the above technical solutions, the embodiment of the present application has the following advantages:
The application provides a power grid fault early warning method based on tide betweenness, which comprises the following steps: solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant; expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function, and constructing a branch equivalent pi-type parameter model; constructing an active forward distribution matrix and a reactive forward distribution matrix based on the load flow state probability density function and the branch equivalent pi-type parameter model; calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively; respectively solving a line load flow medium number and a node load flow medium number according to the transmission active power and the transmission reactive power; and after calculating the severity value of the fault result through the line load flow betweenness and the node load flow betweenness, calculating the fault early warning grade according to the severity value of the fault result according to a preset membership function.
According to the power grid fault early warning method based on the tide medium, the tide state is described as the probability density function in a semi-invariant mode, the uncertainty of a generator set is considered, the power injected into the power grid node is described more pertinently, and the subsequent fault detection result is more accurate and reliable; and (3) carrying out a series of dynamic power flow calculation operations through the power flow state probability density function, quantitatively analyzing the fault result to obtain a determined severity value, and further determining the level of fault early warning according to a preset membership function, thereby not only ensuring the uncertainty of fault detection, but also ensuring the accuracy of the result. Therefore, the application can solve the technical problems of error and lower accuracy of the actual early warning result caused by the fact that the existing deterministic evaluation and probabilistic evaluation methods are either too conservative or lack of quantitative analysis.
Drawings
Fig. 1 is a schematic flow chart of a power grid fault early warning method based on tide betters according to an embodiment of the application;
fig. 2 is another flow chart of a power grid fault early warning method based on tide betters according to an embodiment of the application;
fig. 3 is a schematic structural diagram of a power grid fault early warning device based on tide betters according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an intermediate membership trapezoidal distribution curve according to an embodiment of the present application;
FIG. 5 is a graphical representation of smaller, intermediate and larger membership functions provided by an embodiment of the present application;
Fig. 6 is a schematic topology diagram of an IEEE30 node system according to an application example of the present application;
fig. 7 is a severity rectangular chart of the lines and nodes after the lines 27-28 fail, which is provided by the application example of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and 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 application without making any inventive effort, are intended to be within the scope of the application.
For easy understanding, please refer to fig. 1, a first embodiment of a power grid fault early warning method based on tide betters provided by the present application includes:
And step 101, solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant.
The preset node power equation refers to a power change equation of the acquired node after the randomness and time-varying of the system uncertainty factors are comprehensively considered. I.e. the injected power at the node is not deterministic power but power with some randomness effect.
Semi-invariant method may refer to "cumulative amount", a numerical feature of random variables that acts like a moment; the properties are as follows: the cumulative amount of the sum of the independent random variables is equal to the cumulative amount of each random variable added. The use of a semi-invariant solution vector takes into account the effects of uncertainty factors.
And 102, expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function, and constructing a branch equivalent pi-type parameter model.
The preset number of stages may be selected according to practical situations, such as taylor series, cornish-Fisher series, etc., and is not limited herein. The series expression process is to technically develop the state high-order semi-invariant, and the obtained expression function is the probability density function of the tide state.
Converting the active loss of the branch into two ends of the branch as equivalent loads in the equivalent process of the equivalent pi-type parameter model of the branch, and based on the equivalent load, equivalent the whole network into an active lossless network with r nodes; the method is convenient for subsequent tide calculation and accords with the topological characteristics of the real power grid.
And step 103, constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model.
The downstream distribution matrix contains information such as connection relation among nodes, branch power flow direction, node power and the like, and is known to be an asymmetric matrix according to a basic circuit theory.
And 104, calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively.
The forward distribution matrix has the connection relation between the nodes, so that the transmission characteristic between one node and the other node can be expressed, the transmission active power on the node or the branch can be obtained according to the active forward distribution matrix, and the same transmission reactive power also depends on the calculation process.
Step 105, respectively solving a line load flow medium number and a node load flow medium number according to the transmission active power and the transmission reactive power.
The line load flow betweenness is calculated according to the transmission active power, the load flow change condition of each line is expressed, the node load flow betweenness is calculated according to the transmission reactive power, and the load flow change condition among each node is expressed.
And 106, calculating a fault result severity value through the line load flow betweenness and the node load flow betweenness, and then calculating a fault early warning grade according to the fault result severity value according to a preset membership function.
The overload severity of the line can be calculated according to the line load flow betweenness, the voltage out-of-limit severity on the node can be calculated through the node load flow betweenness, and the fault degree of the line and the node is the total fault result severity. The preset membership function is a piecewise function, and fault result severity values are divided into different ranges according to different regional ranges, so that fault early warning grade division is realized. The preset membership functions generally have smaller, larger and middle types, and can be selected according to the early warning requirement of an actual power grid system, and the preset membership functions are not limited in this regard.
According to the power grid fault early warning method based on the tide medium number, the tide state is described as the probability density function in a semi-invariant mode, the uncertainty of a generator set is considered, the power injected into the power grid node is described more pertinently, and the subsequent fault detection result is more accurate and reliable; and (3) carrying out a series of dynamic power flow calculation operations through the power flow state probability density function, quantitatively analyzing the fault result to obtain a determined severity value, and further determining the level of fault early warning according to a preset membership function, thereby not only ensuring the uncertainty of fault detection, but also ensuring the accuracy of the result. Therefore, the embodiment of the application can solve the technical problems of error and lower accuracy of the actual early warning result caused by the fact that the existing deterministic evaluation and probabilistic evaluation methods are either too conservative or lack of quantitative analysis.
For easy understanding, please refer to fig. 2, the present application provides a second embodiment of a power grid fault early warning method based on tide betters, which includes:
and 201, expressing a preset node power equation into a matrix form to obtain a node power vector.
The preset node power equation of the power system is:
Wherein P i、Qi is the active power and the reactive power injected by the node i respectively, and B ij、Gij is the real part and the imaginary part of the admittance matrix between the nodes respectively; u i、Uj is the voltage at node i and node j, respectively, and θ ij is the phase angle.
The preset node power equation is expressed as a matrix form, namely:
W＝h(X)
wherein W is the node power vector, h is the node power function, and X is the node voltage vector.
And 202, carrying out linearization processing on the node power vector by adopting a Taylor series expression mode to obtain a node power change vector.
The Taylor series expansion is adopted in the method, and the linearization treatment can obtain:
ΔW＝J·ΔX
wherein DeltaW and DeltaX are respectively the node power change vector and the node voltage change vector of the system, and J is a Jacobian matrix.
And 203, solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
Solving by adopting a semi-invariant method, namely, random variable expression of injection power of each node based on the semi-invariant method under the influence of uncertain factors:
wherein DeltaW (k) is the state high-order semi-invariant of node injection power, The high-order half invariant of the output power of wind power generation is k, namely the high-order. The high-order semi-invariant of the state variable, namely the state high-order semi-invariant, can be determined by the homogeneity of the semi-invariant:
ΔX(k)＝J-1(k)·ΔW(k)
Wherein the sensitivity matrix J -1 is a matrix formed by the k powers of the elements.
And 204, expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function, and constructing a branch equivalent pi-type parameter model.
Aiming at the state high-order semi-invariant, the series expression can be adopted as a power flow state probability density function, the Cornish-Fisher series is selected to obtain a probability density function f (eta), the state variable can be specifically set as x, the mean value and the variance of the state variable are n and eta, then the standard form of x is eta= (x-n)/delta, and then the series expansion operation is carried out:
wherein, Gamma s is the s-order semi-invariant of the standard random variable eta, which is the probability density function of the standard normal distribution.
And 205, constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model.
The power change condition of the injection on the node can be determined based on the power flow state probability density function, so that the total injection power of the node is determined, and the branch equivalent pi-type parameter model can simplify the power transmission between the branch and the node and is convenient for power flow calculation. Recording the active downstream distribution matrix asReactive downstream distribution matrix is/>The active forward distribution matrix and the reactive forward distribution matrix are identical in calculation process, and the active forward distribution matrix is taken as an example, and the specific matrix construction process is as follows:
Where i, j=1, 2,..r, P ij (gtoreq.0) is the active power transmitted by node i to node j for line i-j, and P Tj is the total injected active power for node j. In addition, the downstream distribution matrix contains information such as the connection relation among the nodes, the active power flow direction of the branch, the size of the node injection power and the like; obviously, the downstream distribution matrix is an asymmetric matrix.
And 206, calculating an active distribution matrix and an active drawing matrix between the motor and the load according to the active forward distribution matrix.
Step 207, calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix.
The specific calculation process of the active distribution matrix K G and the active drawing matrix K L is as follows:
PLL＝diag[PL1,PL2,...,PLr]
PGG＝diag[PG1,PG2,...,PGr]
Wherein, P LL、PTT and P GG are respectively an active load matrix, a total injection active power matrix and a generator active power matrix, P Lr is an active load of a node r, P Tr is a total injection active power of the node r, P Gr is a generator active power of the node r, and A P is an active forward distribution matrix. The active output of the generator is the active output taking into account the uncertainty factor of the generator set, and if the active output is the wind generator set, the uncertainty factor is generally the wind speed.
The transmission of active power refers to the active power transmitted by the generator m and the load n:
P(m,n)＝KL(n,m)*PGm
Where K L (n, m) is the value of the load versus the nth row and the mth column in the generator active draw matrix K L, and P Gm is the generator active force at node m.
Specific line active components P l (m, n) can be calculated for an active lossless network:
Pl(m,n)＝KL(n,J(l))*KG(m,I(l))*Pl
wherein I (l) is one end of the line l, which is in active flow, J (l) is one end of the line l, which is out active flow, and P l is the line active power flow.
And step 208, calculating a reactive power distribution matrix and a reactive power drawing matrix between the motor and the load according to the reactive power forward distribution matrix.
Step 209, calculating the transmission reactive power between the motor and the load based on the reactive power distribution matrix and the reactive power draw matrix.
The calculation process of the reactive power distribution matrix, the reactive power drawing matrix and the reactive power transmission is the same as the active related calculation process, and is not described herein again, and reactive corresponding quantities can be obtained respectively.
Step 210, respectively solving a line load flow betweenness and a node load flow betweenness according to the transmission active power and the transmission reactive power.
The line load flow betweenness after the fault is calculated according to the transmission active power, and the node load flow betweenness is calculated according to the transmission reactive power. Specifically, the calculation process of the line tide betweenness is as follows:
Wherein F pl is the power flow medium number of the line L, G P is the generator node set, L P is the active load node set, P (m, n) is the active power transmitted by the generator m to the load n, P l (m, n) is the component of the active power transmitted by the generator m to the load n on the line L, min (P m,Pn) is the weight of the active power flow medium number, and the actual active power output of the generator m and the smaller value of the actual active load of the load n are taken to represent the maximum transmissible active power between m and n.
The calculation process of the node tide betweenness is as follows:
Wherein F i is the power flow betweenness of the node i, G Q is the reactive power supply node set, L Q is the reactive load node set, min (Q m,Qn) is the smaller value of the actual reactive power output of the reactive power supply m and the actual reactive load of the load n, the maximum transmissible reactive power between m and n is represented by L being the line between the node i and the node j, the node j represents all the nodes directly connected with the node i by branches, Q (m, n) is the reactive power transmitted by the reactive power supply m to the load n, and Q l' (m, n) represents the part of the reactive power supply m borne by the line L which is injected into (or flows out of) the reactive power transmitted by the load n; the amount of reactive power transmitted to the load n by the reactive power source m via the node i. The process of obtaining Q (m, n) and Q l' (m, n) is the same as the active related power flow calculation process in the line power flow betweenness, and is not described here.
Step 211, calculating an overload severity index value and a node voltage out-of-limit severity index value of the line respectively through the line load flow betweenness and the node load flow betweenness.
The line overload severity index value is obtained through line load flow medium number calculation, the node voltage out-of-limit severity index value is obtained through node load flow medium number calculation, and the specific line overload severity index value calculation process is as follows:
Sev—cur(l)＝FPl·uP[w(LPl)]
Wherein, L Pl is the ratio of the active power of the line L which is not failed after the disconnection fault and the corresponding active power limit value, and w (L Pl) is the active overload loss value of the line L; u P is the voltage on line l.
The calculation process of the node voltage out-of-limit severity index value comprises the following steps:
Sev_U(i)＝Fi·uU[w(Ui)]
Wherein, U i is the ratio of the voltage of the node i after the fault to the rated voltage thereof, w (U i) is the out-of-limit loss value of the voltage of the node i, and U U is the node voltage.
And 212, obtaining the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value.
The specific calculation process is as follows:
Risk_line(l)＝Sev_cur(l)+Sev_U(i)。
step 213, calculating a fault early warning grade according to a preset membership function and a fault result severity value, wherein the preset membership function is as follows:
wherein x is a severity value of the fault result, a, b, c, d is a preset level boundary value.
Substituting the calculated R isk_line (l) as x into the preset membership function to obtain a specific fault early warning grade. The preset membership function selected in this embodiment is an intermediate membership function, and the shape of the trapezoidal curve of the intermediate membership function can be understood with reference to fig. 4. Additional smaller and larger membership functions may also be represented by curves, see FIG. 5. FIG. 5 is a graphical representation of the overall curves of smaller, intermediate and larger membership functions. Whereas the smaller membership functions are expressed as:
the larger membership functions are expressed as:
Step 214, providing different early warning prompts according to the fault early warning level, wherein the early warning prompts comprise critical, serious, attention and normal.
And (5) formulating a fault early warning prompt level:
grade IV risk, critical and serious consequences, and immediate solution should be adopted;
class III risks are serious, the consequences are generally serious, and occurrence is avoided as much as possible;
Class ii risk, note that it is allowed to occur under certain conditions, but risk upgrades must be prevented;
the I-level risk is normal, the risk accident result is light, the possibility of developing into a high-level risk exists, and the dispatcher needs to pay attention to prevention.
According to the power grid fault early warning method based on the tide medium number, the tide state is described as the probability density function in a semi-invariant mode, the uncertainty of a generator set is considered, the power injected into the power grid node is described more pertinently, and the subsequent fault detection result is more accurate and reliable; and (3) carrying out a series of dynamic power flow calculation operations through the power flow state probability density function, quantitatively analyzing the fault result to obtain a determined severity value, and further determining the level of fault early warning according to a preset membership function, thereby not only ensuring the uncertainty of fault detection, but also ensuring the accuracy of the result. Therefore, the embodiment of the application can solve the technical problems of error and lower accuracy of the actual early warning result caused by the fact that the existing deterministic evaluation and probabilistic evaluation methods are either too conservative or lack of quantitative analysis.
In order to facilitate understanding, the application also provides a sample of a grid fault early warning method based on tide betweenness, referring to fig. 6, fig. 6 is a topological structure diagram of an IEEE30 node system after wind power is added, and various parameters of a wind turbine generator and a wind power active output probability density function. The node system comprises 30 nodes and 41 branches, wherein 7 nodes are generator nodes. The total injection power at the node 30 has a wind turbine generator output component, the specific wind turbine generator active processing can be determined according to a wind power active output probability density function, and the wind power active output probability density function can be calculated by the following modes:
in order to solve the uncontrollable and random problems of wind power generation, the variable speed constant frequency wind power generator is adopted in the case, so that the active output of wind power generation can be effectively obtained, and then the following steps are provided:
Wherein v t is the wind speed at time t, v ci is the cut-in wind speed, v r is the rated wind speed, a=p r,tvci/(vci-vr) and b=p r,t/(vr-vci) are constants, v co is the cut-out wind speed, P r,t is the rated output power of the generator, and P W,t is the wind power output.
The wind speed adopts the dual-parameter Weibull distribution, and the probability density function is as follows:
Where K is a shape parameter, k= (σ/μ) -1.086, C is a scale parameter, c=μ/[ Γ (1+1/K) ], where μ is the average wind speed, σ is the standard deviation, Γ is the Gamma function.
The wind power active output probability density function can be calculated by the above method:
After the lines 27-28 in fig. 6 fail, the overload severity of the remaining 40 lines in the system that are not failed is normalized, and as shown in fig. 7 (a), it can be observed that the overload severity of the power of the lines 6-10 is highest, because most of the power transmitted from the generator to the load needs to be transmitted through the lines 6-10, which is in a critical position, and the line load flow medium value is higher, if the lines 6-10 are disconnected due to overload, a larger load flow transfer is likely to be caused, and therefore, the branch overload severity value obtained based on the load flow medium value is in the first position. And at the same time, after the line 27-28 fails, as shown in fig. 7 (b), the severity value of the voltage out-of-limit of the node 25 is highest, because the node 25 plays an important role in connection with other parts of the system by the nodes 26, 27, 29 and 30, and when the voltage out-of-limit occurs in the node 25, the voltage change of the adjacent node is directly affected.
Then, the lines in the IEEE30 node system are sequentially subjected to fault analysis, risk values based on tide betweenness under different line faults can be obtained, and the lines with early warning grades and top 8 bits are listed in the table 1. This example is only for illustrating the feasibility of the present invention, and does not limit the application scenario of the present invention.
Table 1 fault risk ranking list
For easy understanding, referring to fig. 3, the present application further provides an embodiment of a power grid fault early warning device based on a tide betweenness, which includes:
The first calculation module 301 is configured to solve a node power change vector calculated according to a preset node power equation based on a semi-invariant method, so as to obtain a state high-order semi-invariant;
the model construction module 302 is configured to express the state high-order semi-invariant according to a preset series number to obtain a probability density function of the power flow state, and then construct a branch equivalent pi-type parameter model;
The matrix construction module 303 is configured to construct an active downstream distribution matrix and a reactive downstream distribution matrix based on the probability density function of the power flow state and the branch equivalent pi-type parameter model;
The second calculation module 304 is configured to calculate corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix, respectively;
A betting number obtaining module 305, configured to obtain a line load flow betting number and a node load flow betting number according to the transmission active power and the transmission reactive power, respectively;
The level early-warning module 306 is configured to calculate a fault result severity value according to the line load flow betweenness and the node load flow betweenness, and then calculate a fault early-warning level according to the fault result severity value according to a preset membership function.
Further, the first computing module 301 includes:
the expression conversion submodule 3011 is used for expressing a preset node power equation into a matrix form to obtain a node power vector;
the linear processing sub-module 3012 is configured to perform linearization processing on the node power vector by using a taylor series expression mode to obtain a node power variation vector;
And the vector solving submodule 3013 is used for solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
Further, the second computing module 304 includes:
an active matrix calculation submodule 3041, configured to calculate an active distribution matrix and an active drawing matrix between the motor and the load according to the active forward distribution matrix;
an active power calculation submodule 3042 for calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix;
The reactive matrix calculation submodule 3043 is used for calculating a reactive distribution matrix and a reactive drawing matrix between the motor and the load according to the reactive downstream distribution matrix;
A reactive power calculation submodule 3044 for calculating a transmission reactive power between the motor and the load based on the reactive distribution matrix and the reactive drawing matrix.
Further, the level pre-warning module 306 includes:
The primary severity calculation sub-module 3061 is used for calculating a line overload severity index value and a node voltage out-of-limit severity index value through the line load flow betweenness and the node load flow betweenness respectively;
the fault severity calculation sub-module 3062 is used for obtaining the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value;
The fault level dividing sub-module 3063 is configured to calculate a fault early warning level according to a preset membership function and a fault result severity value, where the preset membership function is:
wherein x is a severity value of the fault result, a, b, c, d is a preset level boundary value.
Further, the method further comprises the following steps:
The early warning prompt module 307 is configured to provide different early warning prompts according to the fault early warning level, where the early warning prompts include critical, serious, attention and normal.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for executing all or part of the steps of the method according to the embodiments of the present application by means of a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims (10)

1. A power grid fault early warning method based on tide betweenness is characterized by comprising the following steps:
solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant;
Expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function, and constructing a branch equivalent pi-type parameter model;
constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model;
calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively;
Respectively solving a line load flow betweenness and a node load flow betweenness according to the transmission active power and the transmission reactive power, wherein the line load flow betweenness is calculated according to the transmission active power, and the node load flow betweenness is calculated according to the transmission reactive power;
and calculating a fault result severity value through the line load flow betweenness and the node load flow betweenness, and then calculating a fault early warning grade according to the fault result severity value according to a preset membership function.
2. The power grid fault early warning method based on the tide betweenness according to claim 1, wherein the solving the node power change vector calculated according to the preset node power equation based on the semi-invariant method to obtain the state high-order semi-invariant comprises:
Expressing a preset node power equation into a matrix form to obtain a node power vector;
linearizing the node power vector by adopting a Taylor series expression mode to obtain a node power change vector;
and solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
3. The power grid fault early warning method based on the tide betweenness according to claim 1, wherein the calculating the corresponding transmission active power and the transmission reactive power according to the active downstream distribution matrix and the reactive downstream distribution matrix respectively includes:
Calculating an active distribution matrix and an active drawing matrix between the motor and the load according to the active forward distribution matrix;
calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix;
Calculating a reactive power distribution matrix and a reactive power drawing matrix between the motor and the load according to the reactive power forward distribution matrix;
and calculating the transmission reactive power between the motor and the load based on the reactive power distribution matrix and the reactive power drawing matrix.
4. The grid fault early warning method based on the tide betweenness according to claim 1, wherein after the fault result severity value is calculated through the line tide betweenness and the node tide betweenness, the fault early warning level is calculated according to the fault result severity value according to a preset membership function, and the method comprises the following steps:
calculating an overload severity index value of the line and an out-of-limit severity index value of the node voltage respectively through the line load flow betweenness and the node load flow betweenness;
Solving the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value;
calculating a fault early warning grade according to a preset membership function according to the fault result severity value, wherein the preset membership function is as follows:
wherein x is the severity value of the fault result, a, b, c, d is a preset level boundary value.
5. The grid fault early warning method based on the tide betweenness according to claim 1, wherein after the fault result severity value is calculated through the line tide betweenness and the node tide betweenness, a fault early warning grade is calculated according to a preset membership function and the fault result severity value, and then the method further comprises:
And providing different early warning prompts according to the fault early warning level, wherein the early warning prompts comprise critical, serious, attention and normal.
6. A power grid fault early warning device based on tide betweenness is characterized by comprising:
the first calculation module is used for solving a node power change vector calculated according to a preset node power equation based on a semi-invariant method to obtain a state high-order semi-invariant;
The model construction module is used for expressing the state high-order semi-invariant according to a preset series to obtain a power flow state probability density function and then constructing a branch equivalent pi-type parameter model;
the matrix construction module is used for constructing an active forward distribution matrix and a reactive forward distribution matrix based on the tidal current state probability density function and the branch equivalent pi-type parameter model;
The second calculation module is used for calculating corresponding transmission active power and transmission reactive power according to the active forward distribution matrix and the reactive forward distribution matrix respectively;
the medium number obtaining module is used for obtaining line load flow medium numbers and node load flow medium numbers according to the transmission active power and the transmission reactive power respectively, wherein the line load flow medium numbers are obtained by calculation according to the transmission active power, and the node load flow medium numbers are obtained by calculation according to the transmission reactive power;
And the grade early warning module is used for calculating the fault result severity value according to the preset membership function after calculating the fault result severity value through the line load flow betweenness and the node load flow betweenness, and calculating the fault early warning grade according to the fault result severity value.
7. The power grid fault early warning device based on tide betting as claimed in claim 6, wherein the first calculation module comprises:
The expression conversion sub-module is used for expressing a preset node power equation into a matrix form to obtain a node power vector;
the linear processing sub-module is used for carrying out linearization processing on the node power vector by adopting a Taylor series expression mode to obtain a node power change vector;
and the vector solving sub-module is used for solving the node power change vector based on a semi-invariant method to obtain a state high-order semi-invariant.
8. The power grid fault early warning device based on tide betting as claimed in claim 6, wherein the second calculation module comprises:
the active matrix calculation sub-module is used for calculating an active distribution matrix and an active drawing matrix between the motor and the load according to the active downstream distribution matrix;
An active power calculation sub-module for calculating the transmission active power between the motor and the load based on the active distribution matrix and the active drawing matrix;
the reactive matrix calculation sub-module is used for calculating a reactive distribution matrix and a reactive drawing matrix between the motor and the load according to the reactive downstream distribution matrix;
and the reactive power calculation sub-module is used for calculating the transmission reactive power between the motor and the load based on the reactive power distribution matrix and the reactive power drawing matrix.
9. The power grid fault early warning device based on tide betting as claimed in claim 6, wherein the level early warning module comprises:
the primary severity calculation sub-module is used for calculating a line overload severity index value and a node voltage out-of-limit severity index value through the line load flow betweenness and the node load flow betweenness respectively;
the fault severity computing sub-module is used for obtaining the sum of the line overload severity index value and the node voltage out-of-limit severity index value to obtain a fault result severity value;
The fault grade dividing sub-module is used for calculating a fault early warning grade according to a preset membership function according to the fault result severity value, wherein the preset membership function is as follows:
wherein x is the severity value of the fault result, a, b, c, d is a preset level boundary value.
10. The power grid fault early warning device based on tide betting as claimed in claim 6, further comprising:
and the early warning prompt module is used for providing different early warning prompts according to the fault early warning level, and the early warning prompts comprise critical, serious, attention and normal.
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