CN110414816A - One kind being based on least square power system state estimation method - Google Patents

Abstract

The invention discloses one kind to be based on least square power system state estimation method, is related to electric system, comprising: firstly, determining that identified parameters matrix is η_(n)=(- [Y_LL]^‑1Y_LG‑[Y_LL]^‑1)；Then, the supply voltage phasor on engine nodal, load voltage and electric current phasor on load bus are acquired；Then, identified parameters matrix η is solved_(n)Estimated valueFrom matrix to be identifiedIn split out lotus hair admittance matrix Y_LG, admittance matrix Y between the lotus_LL, solve the lotus hair admittance matrix Y_LGThe change rate of interior items；Finally, extracting lotus sends out admittance matrix Y_LGInterior change rate is greater than the failure item of preset value, and the determining first transmission line road to match with failure item is faulty line.The present invention rejects preceding item data, so that computational throughput tails off, will reject apart from the farther away data at current time, effectively improves system estimation precision, can effectively recognize the failure route of electric system.

Description

Least square-based power system state estimation method

Technical Field

The invention relates to the field of power systems, in particular to a least square-based power system state estimation method.

Background

The electric power system is an electric energy production and consumption system which consists of links such as a power plant, a power transmission and transformation line, a power supply and distribution station, power utilization and the like. The function of the device is to convert the primary energy of the nature into electric energy through a power generation device, and then supply the electric energy to each user through power transmission, power transformation and power distribution. In order to realize the function, the power system is also provided with corresponding information and control systems at each link and different levels, and the production process of the electric energy is measured, regulated, controlled, protected, communicated and scheduled so as to ensure that users obtain safe and high-quality electric energy.

The main structures of the power system include a power source (power plants such as hydropower stations, thermal power plants, and nuclear power plants), a substation (a step-up substation, a load center substation, and the like), a power transmission and distribution line, and a load center. The power supply points are also mutually connected to realize the exchange and regulation of electric energy among different regions, thereby improving the safety and the economical efficiency of power supply. The network formed by the transmission line and the substation is usually called a power network. The information and control system of the power system consists of various detection devices, communication devices, safety protection devices, automatic control devices and automatic monitoring and dispatching systems. The structure of the power system should ensure reasonable coordination of power generation and consumption on the basis of advanced technical equipment and high economic benefit.

A typical power system model includes a plurality of engine nodes, a plurality of load nodes, and a power network.

Because the power system is a complex time-varying system, the data processing amount is larger and larger along with the operation of the system, and the required computer processing capacity for processing data is greatly increased, so that the data processing speed is reduced or the data processing cost is increased. In addition, in the prior art, a forgetting factor is used for weighting to perform recursive least square method, on one hand, old data cannot be completely eliminated, and on the other hand, in some prior arts, the total weighted value of the old data and new data is greater than 1, so that the solved value is shifted.

Disclosure of Invention

In view of some of the above-mentioned drawbacks of the prior art, the present invention provides a method for estimating a state of an electric power system based on least squares, which aims to optimize the solution of parameter estimation of an electric power system network, only retain the most recent data, perform parameter identification, and increase the solution speed of system parameter estimation.

In order to achieve the above object, the present invention provides a least square-based power system state estimation method, including:

step S1, determining the identification parameter matrix as eta_(n)＝(-[Y_LL]^-1Y_LG-[Y_LL]^-1) (ii) a Said Y is_LLIs an inter-load admittance matrix Y between load nodes of the power system_LLSaid Y is_LGFor load transmitting admittance matrix Y between load node and generator node of power system_LG(ii) a The model of the power system satisfies: being a full admittance matrix of the power system, E, I_GGenerator node voltage phasor and generator node current phasor respectively, and V, I load node voltage phasor and load node current phasor respectively;

step S2, collecting power supply voltage phasor E on each engine node on the power system network_nLoad voltage V on the load node_nAnd the current phasor I at the load node_n(ii) a The supply voltage phasor E_n＝[E_(1,n),E_(2,n),...,E_(i,n)]^TA load voltage V on said load node_n＝[V_(1,n),V_(2,n),...,V_(j,n)]^TCurrent phasor I at the load node_n＝[I_(1,n),I_(2,n),...,I_(j,n)]^T(ii) a I isThe node number of the generator node, wherein j is the node number of the load node, and n is the serial number of the sampling data;

step S3, responding to n>p, solving for eta after increasing nth sampling data_{(n-1,add n)}Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

the above-mentionedV _n＝[V_n-p+1 V_n-p+2 ... V_n]SaidThe above-mentionedI is an identity matrix; p is a preset positive integer; the p is greater than the number of parameters to be estimated anda full rank;

step S4, responding to n>p and theSolving the identification parameter matrix eta after removing the n-p times of sampling data_(n)Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

step S5, responding to the matrix to be identifiedThe data in the matrix to be identified fluctuatesAfter stabilization, from the matrix to be identifiedSplitting the load admittance matrix Y_LGThe inter-charge admittance matrix Y_LLSolving said load admittance matrix Y_LGThe rate of change of the items within;

step S6, extracting the load admittance matrix Y_LGAnd acquiring a fault item with an internal change rate larger than a preset value from a first load node and a second generator node corresponding to the fault item, and determining a first transmission line between the first load node and the second generator node as a fault line.

In one embodiment, in the step S2, the power voltage phasor E_nLoad voltage V on the load node_nAnd the current phasor I at the load node_nIs synchronous sampling.

In one embodiment, in the step S2, the power voltage phasor E_nLoad voltage V on the load node_nAnd the current phasor I at the load node_nThe sampling period of (2) is 10ms-1000 ms.

In one embodiment, in the step S5, the charging admittance matrix Y_LGThe rate of change of each item in the table is dataThe ratio of the first steady state before the fluctuation occurs to the second steady state after the data fluctuation occurs.

In a specific embodiment, the step S6 further includes:

obtaining the load admittance matrix Y_LGThe load node number and the engine node number of the fault item, a first load node is determined according to the load node number, and a second generator node is determined according to the engine node number.

The invention has the beneficial effects that: in the invention, the upper limit of the data is kept to be p items by limiting the number of data items of the power system, namely, in the actual operation, the nth sampling data H can be increased firstly_n、V_nThen deleting the first data of the current sequence and maintaining the length of the data sequence. On one hand, the invention eliminates the previous data, so that the calculation processing amount is reduced, and meanwhile, as the power system is a gradual change system, the data accuracy is lower when the power system is farther from the current time node, the farther data from the current time is eliminated, and the system precision is effectively improved. In addition, the two-step solution is carried out through the formula, the higher the obtained parameter estimation precision is, and the parameter estimation accuracy is enhanced. The invention can effectively identify the fault route of the power system on line, improve the efficiency of fault identification and reduce the fault maintenance cost of the power system.

Drawings

FIG. 1 is a schematic flow chart of a least squares based power system state estimation method according to an embodiment of the present invention;

FIG. 2 is a model of a multi-source, multi-load power system in accordance with an embodiment of the present invention.

Detailed Description

The invention is further illustrated by the following examples in conjunction with the accompanying drawings:

the power system is a complex time-varying system, such as a multi-source and multi-load power system shown in fig. 2, the total number of generators is i, the total number of loads is j, a node admittance matrix is introduced, and a power network model based on the whole network can be obtained based on kirchhoff voltage and current laws:

the following can be obtained:

V＝-[Y_LL]^-1Y_LGE-[Y_LL]^-1I (1)

obtaining power supply voltage phasor E on each engine node through continuous sampling of synchronous Phasor Measurement Unit (PMU) on network node of power system_nLoad voltage V on the load node_nAnd the current phasor I at the load node_n(ii) a The supply voltage phasor E_n＝[E_(1,n),E_(2,n),...,E_(i,n)]^TA load voltage V on said load node_n＝[V_(1,n),V_(2,n),...,V_(j,n)]^TCurrent phasor I at the load node_n＝[I_(1,n),I_(2,n),...,I_(j,n)]^T(ii) a The i is the node number of the generator node, the j is the node number of the load node, and the n is the serial number of the sampling data;

and (3) simultaneously obtaining the comprehensive power grid model by the n times of sampling data:

namely:

note the book

According to the least square method, forV _n＝η_(n) H _nIn a term of (A), (B), (C), (H _n)(H _n)^TWhen the device is not in a strange state,obtaining a minimum value:

for the power system model, the model is the same asH _n、V _nIncreasing the nth sample data[V_n]Then, the following are respectively changed:

V _{n-1_add_n}＝[V_n-p V_n-p+1 ... V_n-1 V_n] (7)

order toThe following can be obtained:

H _{n-1_add_n}＝[H _n-1 H_n] (8)

V _{n-1_add_n}＝[V _n-1 V_n] (9)；

then η_{(n-1,add n)}Is estimated value ofSatisfies the following conditions:

according to a matrix inversion formula, the method is simplified to obtain:

in the same way, the above steps are carried outH _{n-1_add_n}、V _{n-1_add_n}Delete leader, getH _n、V _n(ii) a Wherein,

V _{n-1_add_n}＝[V_n-p V_n-p ... V_n-1 V_n] (13)

V _n+1＝[V_n-p+1 V_n-p+2 ... V_n] (15)

namely:

H _{n-1_add_n}＝[H_n-p H _n] (16)

V _{n-1_add_n}＝[V_n-p V _n] (17)

then η_{(n-1,add n)}Is estimated value ofSatisfies the following conditions:

according to a matrix inversion formula, the method is simplified to obtain:

in the invention, the upper limit of the data is kept to be p items by limiting the number of data items of the power system, namely, in the actual operation, the nth sampling data H can be increased firstly_n、V_nThen deleting the first data of the current sequence and maintaining the length of the data sequence.

On one hand, the previous data are removed, so that the calculation processing amount is reduced, and meanwhile, as the power system is a gradual change system, the data accuracy is lower when the power system is farther from the current time node, the data farther from the current time are removed, and the system precision is effectively improved. In addition, the two-step solution is carried out through the formula, the higher the obtained parameter estimation precision is, and the parameter estimation accuracy is enhanced.

Specifically, as shown in fig. 1, in a first example of the present invention, there is provided a least-squares-based power system state estimation method, the method including:

step S1, determining the identification parameter matrix as eta_(n)＝(-[Y_LL]^-1Y_LG-[Y_LL]^-1) (ii) a Said Y is_LLIs an inter-load admittance matrix Y between load nodes of the power system_LLSaid Y is_LGFor load transmitting admittance matrix Y between load node and generator node of power system_LG(ii) a The model of the power system satisfies: being a full admittance matrix of the power system, E, I_GGenerator node voltage phasor and generator node current phasor respectively, and V, I load node voltage phasor and load node current phasor respectively;

step S2, collecting power supply voltage phasor E on each engine node on the power system network_nLoad voltage V on the load node_nAnd the current phasor I at the load node_n(ii) a The supply voltage phasor E_n＝[E_(1,n),E_(2,n),...,E_(i,n)]^TA load voltage V on said load node_n＝[V_(1,n),V_(2,n),...,V_(j,n)]^TCurrent phasor I at the load node_n＝[I_(1,n),I_(2,n),...,I_(j,n)]^T(ii) a The i is the node number of the generator node, the j is the node number of the load node, and the n is the serial number of the sampling data;

step S3, responding to n>p, solving for eta after increasing nth sampling data_{(n-1,add n)}Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

the above-mentionedV _n＝[V_n-p+1 V_n-p+2 ...V_n]SaidThe above-mentionedI is an identity matrix; p is a preset positive integer;

step S4, responding to n>p and theSolving the identification parameter matrix eta after removing the n-p times of sampling data_(n)Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

step S5, responding to the matrix to be identifiedThe data in the matrix to be identified fluctuatesAfter stabilization, from the matrix to be identifiedSplitting the load admittance matrix Y_LGThe inter-charge admittance matrix Y_LLSolving said load admittance matrix Y_LGThe rate of change of the items within;

step S6, extracting the load admittance matrix Y_LGAnd acquiring a fault item with an internal change rate larger than a preset value from a first load node and a second generator node corresponding to the fault item, and determining a first transmission line between the first load node and the second generator node as a fault line.

In this embodiment, in the step S2, the power supply voltage phasor E_nLoad voltage V on the load node_nAnd the current phasor I at the load node_nIs synchronous sampling.

In this embodiment, in the step S2, the power supply voltage phasor E_nLoad voltage V on the load node_nAnd the current phasor I at the load node_nThe sampling period of (2) is 10ms-1000 ms.

In this embodiment, in the step S5, the load admittance matrix Y_LGThe change rate of each item in the data storage is the ratio of a first stable state before the data fluctuates to a second stable state after the data fluctuates.

In this embodiment, the step S6 further includes:

obtaining the load admittance matrix Y_LGThe load node number and the engine node number of the fault item, a first load node is determined according to the load node number, and a second generator node is determined according to the engine node number.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (5)

1. A least squares based power system state estimation method, the method comprising:

step S1, determining the identification parameter matrix as eta_(n)＝(-[Y_LL]^-1Y_LG -[Y_LL]^-1) (ii) a Said Y is_LLIs an inter-load admittance matrix Y between load nodes of the power system_LLSaid Y is_LGFor load transmitting admittance matrix Y between load node and generator node of power system_LG(ii) a The model of the power system satisfies: being a full admittance matrix of the power system, E, I_GGenerator node voltage phasor and generator node current phasor respectively, and V, I load node voltage phasor and load node current phasor respectively;

step S2, collecting power supply voltage phasor E on each engine node on the power system network_nLoad voltage V on the load node_nAnd the current phasor I at the load node_n(ii) a The supply voltage phasor E_n＝[E_(1,n),E_(2,n),...,E_(i,n)]^TA load voltage V on said load node_n＝[V_(1,n),V_(2,n),...,V_(j,n)]^TCurrent phasor I at the load node_n＝[I_(1,n),I_(2,n),...,I_(j,n)]^T(ii) a The i is the node number of the generator node, the j is the node number of the load node, and the n is the serial number of the sampling data;

step S3, responding to n>p, solving for eta after increasing nth sampling data_{(n-1,add n)}Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

the above-mentionedV _n＝[V_n-p+1 V_n-p+2 ... V_n]SaidThe above-mentionedI is an identity matrix; p is a preset positive integer;

step S4, responding to n>p and theSolving the identification parameter matrix eta after removing the n-p times of sampling data_(n)Is estimated value ofThe above-mentionedSatisfies the following conditions:

wherein,

step S5, responding to the matrix to be identifiedThe data in the matrix to be identified fluctuatesAfter stabilization, from the matrix to be identifiedSplitting the load admittance matrix Y_LGThe inter-charge admittance matrix Y_LLSolving said load admittance matrix Y_LGThe rate of change of the items within;

step S6, extracting the load admittance matrix Y_LGAnd acquiring a fault item with an internal change rate larger than a preset value from a first load node and a second generator node corresponding to the fault item, and determining a first transmission line between the first load node and the second generator node as a fault line.

2. The least squares-based power system state estimation method of claim 1, wherein in the step S2, the supply voltage phasor E_nLoad voltage V on the load node_nAnd the current phasor I at the load node_nIs synchronous sampling.

3. The least squares-based power system state estimation method of claim 1, wherein in the step S2, the supply voltage phasor E_nNegative on the load nodeCharged voltage V_nAnd the current phasor I at the load node_nThe sampling period of (2) is 10ms-1000 ms.

4. The least squares-based power system state estimation method of claim 1, wherein in the step S5, the load admittance matrix Y_LGThe change rate of each item in the data storage is the ratio of a first stable state before the data fluctuates to a second stable state after the data fluctuates.

5. The least squares-based power system state estimation method of claim 1, wherein the step S6 further comprises:

obtaining the load admittance matrix Y_LGThe load node number and the engine node number of the fault item, a first load node is determined according to the load node number, and a second generator node is determined according to the engine node number.