CN112152198B - Low-model-dependency intelligent step length adjustment state estimation method and system for power system - Google Patents

Low-model-dependency intelligent step length adjustment state estimation method and system for power system Download PDF

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CN112152198B
CN112152198B CN202010806544.2A CN202010806544A CN112152198B CN 112152198 B CN112152198 B CN 112152198B CN 202010806544 A CN202010806544 A CN 202010806544A CN 112152198 B CN112152198 B CN 112152198B
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胡斯佳
李勇
李胜
曹一家
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a low-model dependency intelligent step length adjustment state estimation method and system for an electric power system. Due to the low coupling degree with the power system mathematical model, the method shows high numerical stability and estimation quality under various complex conditions (including bad data and network ill-condition), which is incomparable with the traditional method with high power system model dependency.

Description

Low-model-dependency intelligent step length adjustment state estimation method and system for power system
Technical Field
The invention relates to the field of power system state estimation, in particular to a low-model dependency intelligent step length adjustment state estimation method and system for a power system.
Background
The power system state estimation is an important component of the energy management system, and provides reliable and complete system operation state information for the power system. In the actual operation of the power grid, the ill-conditioned power flow problem can be caused by various adverse factors in the power grid, and the optimal multiplier method is an effective method for processing the ill-conditioned power flow. In addition, the step optimization technology is commonly used for improving the convergence and the robustness of the pathological trend and obtaining a good effect. However, until now, there has been little research associated therewith in the field of power system state estimation.
In practical power system state estimation, the traditional method is to fix the step factor of the state correction equation to 1, but in actual execution, the algorithm cannot guarantee effective convergence of the system due to low measured data quality and complex network conditions. In order to improve the convergence performance of state estimation and improve the estimation quality, researchers provide a state estimation optimal step factor method, the convergence performance of the state estimation optimal step factor method is superior to that of a traditional method when bad data occur, but the method does not consider the operation performance under the network ill-condition and is highly related to a power system model, the calculation process is complex, and the calculation amount is large. In addition, for the problem of power system state estimation, the network naturally has a 'numerical calculation defect' and the external input is hard to distinguish, and the optimal step length method which excessively depends on the model often shows abnormal conditions such as slow convergence, oscillation, divergence and the like. Therefore, it is an important research subject in the field to improve the adaptability of state estimation, improve the numerical stability of state estimation under these circumstances, and improve the estimation quality.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a method and a system for estimating a low-model-dependency intelligent step-size adjustment state of an electrical power system.
The technical scheme for solving the problems is as follows:
on one hand, the invention provides a low-model-dependency intelligent step length adjustment state estimation method for a power system, which comprises the following steps:
s1, independent variable selection: correcting vector in state
Figure BDA0002629334530000021
Absolute value of each component
Figure BDA0002629334530000022
Take the maximum value, i.e.
Figure BDA0002629334530000023
As an independent variable of the formula, let Δ x be a state correction amount;
s2, designing an algorithm function, including:
s21, an exponential inversion function is constructed, delta x in the iteration process is directly related to a step length adjustment factor lambda, and the specific form is shown as (1):
Figure BDA0002629334530000024
wherein: lambda [ alpha ](k)Adjusting the step size factor for the step k of iteration; Δ x(k)And Δ x(k-1)The state correction quantity of the k step and the k-1 step; xi(k)And xi(k-1)Is the k-th and the k-thk-1 step of iterative intermediate variables; psi, alpha are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in S3;
s22, combining S21, the iterative format for forming the low-model dependency intelligent step length adjustment state estimation of the whole power system is as follows:
Figure BDA0002629334530000025
wherein z is a measurement vector;
Figure BDA0002629334530000031
is by means of state vectors
Figure BDA0002629334530000032
A measurement function of the representation;
Figure BDA0002629334530000033
is a residual vector; r is-1In order to be the weight, the weight is,
Figure BDA0002629334530000034
for the measurement device variance at each measurement point, i ═ 1,2 … m; k is the number of iterations;
Figure BDA0002629334530000035
is composed of
Figure BDA0002629334530000036
M × n-order jacobian matrices;
s3, algorithm parameter selection: to select the parameters psi, alpha, p(0)In the algorithm debugging phase, the parameters psi, alpha, p are found by writing 3 for-cycles in the program(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
On the other hand, the invention provides an intelligent step length adjustment state estimation system with low model dependency for a power system, which comprises an independent variable selection module, an algorithm function design module and an algorithm parameter selection module;
the independent variable selection module is used for correcting the vector in the state
Figure BDA0002629334530000037
Absolute value of each component
Figure BDA0002629334530000038
Take the maximum value, i.e.
Figure BDA0002629334530000039
As an independent variable of the formula, let Δ x be a state correction amount;
the algorithm function design module provides a structural index inversion function, and directly links delta x and a step length adjustment factor lambda in an iteration process, wherein the specific form is shown as (1):
Figure BDA00026293345300000310
wherein: lambda [ alpha ](k)Adjusting the factor for the step length of the step k of iteration; Δ x(k)And Δ x(k-1)The state correction quantity of the k step and the k-1 step; xi(k)And xi(k-1)Is the iterative intermediate variable of the k step and the k-1 step; psi, alpha are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in the algorithm parameter selection module;
combining with the formula (1), the iterative format for forming the low model dependency intelligent step length adjustment state estimation of the whole power system is as follows:
Figure BDA0002629334530000041
wherein z is a measurement vector;
Figure BDA0002629334530000042
is by means of state vectors
Figure BDA0002629334530000043
A measurement function of the representation;
Figure BDA0002629334530000044
is a residual vector; r-1In order to be the weight, the weight is,
Figure BDA0002629334530000045
for the measurement device variance at each measurement point, i ═ 1,2 … m; k is the number of iterations;
Figure BDA0002629334530000046
is composed of
Figure BDA0002629334530000047
M × n-order jacobian matrices;
the algorithm parameter selection module is used for selecting parameters psi, alpha and p(0)In the algorithm debugging phase, the parameters psi, alpha, p are found by writing 3 for-cycles in the program(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
The invention has the beneficial effects that:
when bad data appear in measurement and a network tends to be ill-conditioned, the low-model-dependency intelligent step length adjustment state estimation algorithm of the power system breaks away from a mathematical model of the power system by designing a step length factor of a constructed index inversion function to directly establish a change relation between a state correction quantity delta x and the step length factor lambda so as to form an intelligent adjustment step length factor. At the initial step of iteration, the step length is automatically increased to accelerate the convergence speed; when two adjacent iterations are performed
Figure BDA0002629334530000048
With significant variation (which typically occurs in the intermediate stages of the iteration), the iteration step follows
Figure BDA0002629334530000049
The iteration speed is ensured, and the calculation precision is taken into consideration to a certain extent; when an iteration enters the end-sound (which is generally represented by two adjacent iterations)
Figure BDA00026293345300000410
Less variation) to improve the quality and robustness of the calculation, the algorithm automatically approaches the numerical solution with 'denser' step sizes. The iteration behavior is the adjustment automatically made by the state estimation solver according to the iteration effect, and the coupling degree of the faced power system model is very low, so that the numerical stability and the estimation quality of the state estimation can be greatly improved under more diversified complex power grid environments by adopting the method.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a block diagram of a low model dependency intelligent step size adjustment state estimation system for a power system according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It should be noted that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the protection scope of the present invention.
Example 1
The invention provides a low-model-dependency intelligent step length adjustment state estimation method for an electric power system, which mainly comprises the following steps of: independent variable selection, algorithm function design and algorithm parameter selection.
Independent variable selection: is in-state correction vector
Figure BDA0002629334530000051
Absolute value of each component
Figure BDA0002629334530000052
Take the maximum value, i.e.
Figure BDA0002629334530000053
As an argument of the formula, Δ x is referred to as a state correction amount.
The algorithm function design comprises two parts:
a first part: providing a structural exponential inversion function, and correcting the state correction quantity delta x of the k step and the k-1 step in the iterative process(k)And Δ x(k-1)Step size adjustment factor lambda with k(k)The contact is directly obtained, and the specific form is as follows:
Figure BDA0002629334530000061
wherein: xi(k)And xi(k-1)Is the iterative intermediate variable of the k step and the k-1 step; psi, alpha are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in the "algorithm parameter selection module". When abs (Δ x) is actually performed(k)/Δx(k-1))2When the value is 10 or more, abs (. DELTA.x)(k)/Δx(k-1))2When the value is less than or equal to 0.2, the value is 0.2.
A second part: forming an iteration format of the low model dependency intelligent step length adjustment state estimation of the power system:
Figure BDA0002629334530000062
wherein z is a measurement vector;
Figure BDA0002629334530000063
is by means of state vectors
Figure BDA0002629334530000064
A measurement function of the representation;
Figure BDA0002629334530000065
is a residual vector; r-1In order to be the weight, the weight is,
Figure BDA0002629334530000066
for the measurement device variance at each measurement point, i ═ 1,2 … m; k is the number of iterations;
Figure BDA0002629334530000067
is composed of
Figure BDA0002629334530000068
M × n-order jacobian matrix. Because the lambda is directly adjusted in a multi-azimuth and automatic intelligent mode according to the change condition of the delta x, the step length of the method has low dependency on a mathematical model of the power system.
Selecting algorithm parameters: to select the parameters psi, alpha, p(0)By programming 3 for-loops in the program to find the parameters ψ, α, p(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
Example 2
As shown in fig. 1, the invention provides a low-model-dependency intelligent step-length adjustment state estimation system for an electric power system, which comprises an independent variable selection module, an algorithm function design module and an algorithm parameter selection module;
the independent variable selection module is used for correcting the vector in the state
Figure BDA0002629334530000071
Absolute value of each component
Figure BDA0002629334530000072
Take the maximum value, i.e.
Figure BDA0002629334530000073
As an argument of the formula, Δ x is referred to as a state correction amount.
The algorithm function design module comprises two parts:
a first part: providing a structural exponential inversion function, and correcting the state correction quantity delta x of the k step and the k-1 step in the iterative process(k)And Δ x(k-1)Step size adjustment factor lambda with k(k)The specific form of the contact is as follows:
Figure BDA0002629334530000074
wherein: xi(k)And xi(k-1)Is the iterative intermediate variable of the k step and the k-1 step; psi, alpha are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in the "algorithm parameter selection module". When abs (Δ x) is actually performed(k)/Δx(k-1))2When the value is 10 or more, abs (. DELTA.x)(k)/Δx(k-1))2When the value is less than or equal to 0.2, the value is 0.2.
A second part: forming an iteration format of the low model dependency intelligent step length adjustment state estimation of the power system:
Figure BDA0002629334530000075
wherein z is a measurement vector;
Figure BDA0002629334530000076
is by means of state vectors
Figure BDA0002629334530000077
A measurement function of the representation;
Figure BDA0002629334530000078
is a residual vector; r-1In order to be the weight, the weight is,
Figure BDA0002629334530000081
for the measurement device variance at each measurement point, i ═ 1,2 … m; k is the number of iterations;
Figure BDA0002629334530000082
is composed of
Figure BDA0002629334530000083
M × n-order jacobian matrix. Because the lambda is directly adjusted in a multi-azimuth and automatic intelligent mode according to the change condition of the delta x, the step length of the method has low dependency on a mathematical model of the power system.
The algorithm parameter selection module is used for selecting parameters psi, alpha and p(0)By programming 3 for-loops in the program to find the parameters ψ, α, p(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (2)

1. A low-model-dependency intelligent step length adjustment state estimation method for a power system is characterized by comprising the following steps of:
s1, independent variable selection: correcting vector in state
Figure FDA0003377026670000011
Absolute value of each component
Figure FDA0003377026670000012
Take the maximum value, i.e.
Figure FDA0003377026670000013
As an independent variable of the formula, let Δ x be a state correction amount;
s2, designing an algorithm function, including:
s21, providing a constructed index inversion function, and directly establishing a functional relation between delta x and a step length adjustment factor lambda in an iteration process, wherein the specific form is shown as (1):
Figure FDA0003377026670000014
wherein: lambda [ alpha ](k)Adjusting a factor for the step size of the kth iteration; Δ x(k)And Δ x(k-1)State corrections for the kth and kth-1 iteration; xi(k)And xi(k-1)Intermediate variables for the kth and kth-1 iteration; psi, a are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in S3;
s22, combining S21, the iterative format for forming the low-model dependency intelligent step length adjustment state estimation of the whole power system is as follows:
Figure FDA0003377026670000015
wherein z is a power system measurement vector;
Figure FDA0003377026670000016
is a power system state vector
Figure FDA0003377026670000017
A measurement function of the representation;
Figure FDA0003377026670000021
is a residual vector; r-1In order to be the weight, the weight is,
Figure FDA0003377026670000022
Figure FDA0003377026670000023
the variance of a measuring device at each measuring point in the power system is 1,2 … m; k is the number of iterations;
Figure FDA0003377026670000024
is composed of
Figure FDA0003377026670000025
M × n-order jacobian matrices;
s3, selecting algorithm parameters: to select the parameters psi, alpha, p(0)In the algorithm debugging phase, the parameters psi, alpha, p are found by writing 3 for-cycles in the program(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
2. A low-model-dependency intelligent step length adjustment state estimation system of a power system is characterized by comprising an independent variable selection module, an algorithm function design module and an algorithm parameter selection module;
the independent variable selection module is used for correcting the vector in the state
Figure FDA0003377026670000026
Absolute value of each component
Figure FDA0003377026670000027
Take the maximum value, i.e.
Figure FDA0003377026670000028
As an independent variable of the formula, let Δ x be a state correction amount;
the algorithm function design module provides a constructed index inversion function, and a functional relation is directly established between delta x and a step length adjustment factor lambda in an iteration process, wherein the specific form is shown as (1):
Figure FDA0003377026670000029
wherein: lambda [ alpha ](k)Adjusting a factor for the step size of the kth iteration; Δ x(k)And Δ x(k-1)State corrections for the kth and kth-1 iteration; xi shape(k)And xi(k-1)Intermediate variables for the kth and kth-1 iteration; psi, alpha are control parameters, psi, alpha and xi(k)Initial value xi of(0)Will be determined in the algorithm parameter selection module;
combining with the formula (1), the iterative format for forming the low-model dependency intelligent step length adjustment state estimation of the whole power system is as follows:
Figure FDA0003377026670000031
wherein z is a power system measurement vector;
Figure FDA0003377026670000032
is a power system state vector
Figure FDA0003377026670000033
A metrology function of the representation;
Figure FDA0003377026670000034
is a residual vector; r-1In order to be the weight, the weight is,
Figure FDA0003377026670000035
Figure FDA0003377026670000036
the variance of a measuring device at each measuring point in the power system is 1,2 … m; k is the number of iterations;
Figure FDA0003377026670000037
is composed of
Figure FDA0003377026670000038
M × n-order jacobian matrices;
the algorithm parameter selection module is used for selecting parameters psi, alpha and p(0)In the algorithm debugging phase, the parameters psi, alpha, p are found by writing 3 for-cycles in the program(0)If several groups of different optional parameter combinations with the same iteration times exist, the optimal values of the parameters are selected according to the estimated quality of the parameters, and the three parameters are kept unchanged once being determined.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103413053A (en) * 2013-08-21 2013-11-27 国家电网公司 Robust state estimation method based on interior point method for electrical power system
CN104102836A (en) * 2014-07-14 2014-10-15 国家电网公司 Method for quickly estimating robust state of power system
CN107069696A (en) * 2016-09-23 2017-08-18 四川大学 A kind of parallel calculating method of Power system state estimation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103413053A (en) * 2013-08-21 2013-11-27 国家电网公司 Robust state estimation method based on interior point method for electrical power system
CN104102836A (en) * 2014-07-14 2014-10-15 国家电网公司 Method for quickly estimating robust state of power system
CN107069696A (en) * 2016-09-23 2017-08-18 四川大学 A kind of parallel calculating method of Power system state estimation

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