CN111463782B - Voltage sensitive load model and parameter identification method - Google Patents
Voltage sensitive load model and parameter identification method Download PDFInfo
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Abstract
The invention relates to a voltage sensitive load model and a parameter identification method. According to the method, voltage and load power data at a 10/0.4kV transformer are obtained through a power grid measuring device, a traditional load model is improved, a flexible load model suitable for a power distribution network is provided, the correlation between the voltage and a load random part is considered, the voltage and load power data are used as conditions, the minimum correlation between the voltage and the load random part is used as an evaluation function, the improved load model is trained through a particle swarm algorithm, so that the identified parameters can effectively fit the mapping function relationship between the voltage and a voltage sensitive load, the improved static load model parameters are identified, and the voltage sensitive load is separated from the load power. The characteristic coefficient p of the active voltage is then calculateduThe method solves the problem of large parameter errors of the load model caused by low signal-to-noise ratio and large load fluctuation of the power distribution network.
Description
Technical Field
The invention belongs to the field of power system automation, and relates to a voltage sensitive load model and a parameter identification method.
Background
At present, the power growth situation in China is slow, and power surplus occurs in many areas, but local and stage tension states are still possible to occur in the long run. Due to the structural contradiction of the power load, the peak-valley difference of the power grid is in a continuously enlarged state. According to statistics, the accumulated time of the peak load of more than 95% in China is only dozens of hours, so that a large number of peak shaving units are idle and wasted. Meanwhile, with the access of large-scale new energy to the power grid, the characteristics of randomness, intermittence and fluctuation are difficult to peak load shifting, even the phenomenon of negative peak load shifting is caused, and the new energy is difficult to fully utilize. The particularity of the power load configuration therefore determines that it is bound by a plurality of external influencing factors.
The flexible load can make corresponding power regulation according to the requirement within a certain time, and the contradiction of the supply and demand sides is relieved. In the face of the characteristics that a user electricity utilization mode is more flexible and efficient, the method is more and more necessary to mining the potential value of load data by applying data analysis methods such as cluster analysis and correlation analysis to the electricity utilization load under the power big data environment. The accurate and reasonable load model can provide guidance information for system scheduling and operation control, and has wide engineering application prospect. With the rapid development of the related technology of the flexible load, the description capacity of the original load model to the power grid is continuously reduced, and a novel modeling method and strategy which are suitable for the modern power grid are urgently required to be searched.
Disclosure of Invention
In order to solve the above problems, the present invention provides a voltage-sensitive load model and a parameter identification method. The method comprises the steps of arranging measuring devices at certain key nodes, measuring voltage and load power data, improving a traditional load model, providing a flexible load model suitable for a power distribution network, separating voltage sensitive load power from the load power by utilizing correlation analysis through load voltage data information, and then calculating an active voltage characteristic coefficient puThe method solves the problem of large parameter errors of the load model caused by low signal-to-noise ratio and large load fluctuation of the power distribution network. On one hand, the method is beneficial to the electric power department to grasp the composition of each part of the load in the area in time, on the other hand, the method provides data support for load regulation and control, and has certain application value.
The invention provides a voltage sensitive load model and a parameter identification method, which are characterized in that: voltage and load power data at a 10/0.4kV transformer are obtained through a power grid measuring device, correlation between load voltage and random components (including random load fluctuation, measurement noise and constant impedance load) is used as an evaluation function, and an improved load model is trained, so that the mapping function relation between voltage and voltage sensitive load can be effectively fitted through identified parameters. After the model outputs the voltage sensitive load identification, this portion is subtracted from the load power so that the correlation between the remaining random components and the voltage is minimized. After obtaining the voltage sensitive load model, calculating the active voltage characteristic coefficient puAnd data support is provided for load regulation and control.
A voltage sensitive load model and a parameter identification method are characterized by comprising the following steps:
step 1, acquiring voltage and load power data of a 10/0.4kV transformer through a power grid measuring device; and then carrying out data normalization processing on the collected voltage data and load power data.
And 2, selecting an improved load model to fit the mapping relation between the voltage and the voltage sensitive load. Based on the traditional ZIP model shown as a formula (1), an improved model shown as a formula (2) is provided, and a static model of a voltage-sensitive load is established, so that the load is divided into a voltage-sensitive partial load and a voltage-independent load. The adopted model is obtained by improving based on the existing ZIP load model, the traditional ZIP model considers 4 parts of measurement noise, wherein the constant impedance and constant current part is P sensitive to voltageUIs the part that needs to establish the mapping function; the remaining random components of the load (including the constant power) and the measurement noise have no correlation with the voltage fluctuation as shown in equation (3) and thus can be regarded as a whole. Therefore, the improved static load model is that the constant impedance and constant current parts of the original ZIP model are reserved, and the rest parts are separated by adopting a proper method, so that the mapping relation between the voltage and the voltage sensitive load is established.
P＝aU2+bU+c+e (I)
Pu＝Fu(P，U，θP)＝a·U2+b·U (2)
PεC + e (3) wherein: p represents the load power of the low-voltage transformer area; puThe load is a part of the load sensitive to voltage, namely the load with which the power of the load changes when the voltage fluctuates; pεRepresents a voltage independent load, i.e. a load that does not vary with voltage fluctuations; fu(. cndot.) represents a non-linear mapping function between voltage to voltage sensitive load; thetaPParameters contained in the mapping relation; u represents a voltage; a. b, c and e respectively represent the constant impedance load, the constant current load, the constant power load and the coefficient of the measurement noise.
And 3, identifying the voltage sensitive load model parameters by using a particle swarm algorithm. The best position each particle has experienced while continuously searching is defined as an individualOptimum position, denoted as Pbest(ii) a The best position that all particles of the population have undergone is defined as the global optimal position, which is marked as Gbest. All particles have a fitness value determined by an optimization function, and the particles are continuously updated by 2 extreme values during iteration to generate individuals of the next generation. M particles defining a D-dimensional search space, wherein the position of the ith particle is XiThe best past position is noted as Pbest(g) At a velocity of Vi. The speed and position of each particle are adjusted by the following formulas (4) to (5):
wherein the content of the first and second substances,
representing the speed of the ith particle at the ith iteration; omega is an inertia weight, so that the particles can keep the motion inertia; c. C1、c2Is a learning factor, can be set to 2; rand1、rand2Is [0,1 ]]A random number in between; pbest(g) Representing the best position each particle has experienced while continuously looking for; gbest(g) Representing the best positions that all particles of the population have experienced;
indicating the position of the ith particle at the ith iteration.
Step 4, separating out the voltage sensitive load PuThen, deducting the voltage sensitive load P from the load power PuThe voltage U is connected to a negative random component PεThe correlation between the two is taken as an evaluation function of the output result of the particle swarm optimization, wherein the correlation analysis expression is
And evaluating the effectiveness of the output result of the particle swarm algorithm. By continuously searching particles, the voltage U and the negative random component P are foundεThe corresponding model parameter is used as the optimal parameter identification result when the correlation between the model parameters is minimum.
Step 5, after identifying the voltage sensitive load model parameters, calculating an active voltage characteristic coefficient pu。
In the voltage-sensitive load model and the parameter identification method, the specific operation method for performing normalization processing on the collected voltage data and load power data in the step 1 is as follows:
data normalization processing: the normalization processing formula is as follows:
wherein u isjmax、ujminRespectively, the j day voltage Uj0Maximum and minimum values of pjmax、pjminRespectively the j day load power Pj0Maximum and minimum values of.
In the above voltage-sensitive load model and parameter identification method, the specific operation method in step 3 is as follows:
and 3.1, inputting voltage, active power measurement data and parameters to be identified of the static load model. Setting the size of the particle group as m and the initial position xiInitial velocity viAnd limiting the variation range x of position and speedi∈[xmin,xmax]、vi∈[vmin,vmax]. Given inertia weight omega and maximum iteration number NmaxLearning factor c1、c2Initializing Pbest、Gbest。。
And 3.2, defining a fitness function of the particles as shown in the formula (6):
in the formula, N is the number of the measurement data sampling points; u shapekMeasuring the voltage quantity; pkMeasuring active power; f (-) is the function to be identified.
Step 3.3, the fitness value of the particle is compared with the best position P experiencedbestComparing, if it is better than PbestThen replace P with the current fitness valuebest(ii) a Simultaneously with the global best position G experiencedbestIn contrast, if it is better than GbestThen update GbestThe position of (a). The position and velocity of the particle are updated using equations (4) through (5). If the fitness value does not meet the set error limit or the iteration number is smaller than the upper limit, switching to the second step; otherwise, ending the network training and outputting the optimal fitness value and the corresponding model parameter identification result.
In the voltage-sensitive load model and the parameter identification method, the load random component P is obtained in step 4εThe method comprises the following steps:
calculating the voltage sensitive load P by the formula (2)uThen, the random component P of the load is calculated by using the formula (7)ε。
Pε＝P-Pu (7)
In the voltage-sensitive load model and the parameter identification method, the active voltage characteristic coefficient p is obtained in step 5uThe method comprises the following steps:
after identifying the voltage sensitive load model parameters in step 4, calculating the active voltage characteristic coefficient p by using a formula (8)u。
pu＝2×a×U+b (8)
The invention has the characteristics and beneficial effects that: the method considers the correlation between the voltage and the load random part, utilizes the voltage and the load power data as conditions, takes the minimum correlation between the voltage and the load random part as an evaluation function, and identifies the improved static load model parameters through the particle swarm optimization so as to separate the voltage sensitive load from the load power. The method proposed by the invention can be realized by using a negative chargeAnd voltage sensitive load is separated from load power, so that data support is provided for establishment of a flexible load model, load regulation and control and the like. Specifically, the present invention has the following advantages: 1. the data required by the method can be obtained only by installing a measuring device on a 10/0.4kV transformer, and the economical efficiency is good. 2. A traditional static load model is improved, a constant impedance load and a constant current load are used as voltage sensitive loads, and a flexible load model is established. 3. The load fluctuation, the measurement noise and the constant power load are used as load random components and are separated from the load power through correlation, and the accuracy of model parameter identification can be improved. 4. The active voltage characteristic coefficient p can be calculated by providing a voltage sensitive load modeluAnd data support is provided for load regulation and control.
Drawings
Fig. 1 is a schematic view of the measuring device of the present invention in a grid installation location.
FIG. 2 is a flow chart illustrating a parameter identification method according to the present invention.
Detailed Description
The method comprises the steps of analyzing the correlation between voltage and load random components, considering the correlation between the voltage and load random parts, utilizing voltage and load power data as conditions, taking the minimum correlation between the voltage and the load random parts as an evaluation function, training an improved load model through a particle swarm algorithm to enable identified parameters to effectively fit the mapping function relationship between the voltage and the voltage sensitive load, separating the voltage sensitive load from the load power, and then calculating an active voltage characteristic coefficient puAnd data support is provided for load regulation and control. The following is described in connection with the accompanying drawings and examples:
the invention adopts the technical scheme that a voltage sensitive load model and a parameter identification method are characterized by comprising the following steps:
step 1, acquiring voltage and load power data of a 10/0.4kV transformer through a power grid measuring device; and then carrying out data normalization processing on the collected voltage data and load power data. The specific operation method for performing normalization processing on the collected voltage data and load power data is as follows:
data normalization processing: the normalization processing formula is as follows:
wherein u isjmax、ujminRespectively, the j day voltage Uj0Maximum and minimum values of pjmax、pjminRespectively the j day load power Pj0Maximum and minimum values of.
And 2, selecting an improved load model to fit the mapping relation between the voltage and the voltage sensitive load. Based on the traditional ZIP model shown as a formula (9), an improved model shown as a formula (10) is provided, and a static model of the voltage-sensitive load is established, so that the load is divided into a voltage-sensitive partial load and a voltage-independent load. The adopted model is obtained by improving based on the existing ZIP load model, the traditional ZIP model considers 4 parts of measurement noise, wherein the constant impedance and constant current part is P sensitive to voltageUIs the part that needs to establish the mapping function; the remaining random components of the load (including the constant power) and the measurement noise have no correlation with the voltage fluctuation as shown in equation (11) and thus can be regarded as a whole. Therefore, the improved static load model is that the constant impedance and constant current parts of the original ZIP model are reserved, and the rest parts are separated by adopting a proper method, so that the mapping relation between the voltage and the voltage sensitive load is established.
P＝aU2+bU+c+e (9)
Pu＝Fu(P，U，θP)＝a·U2+b·U (10)
Pε(ii) wherein: p represents the load power of the low-voltage transformer area; puThe load is a part of the load sensitive to voltage, namely the load with which the power of the load changes when the voltage fluctuates; pεRepresents a voltage independent load, i.e. a load that does not vary with voltage fluctuations; fu(. cndot.) represents a non-linear mapping function between voltage to voltage sensitive load; thetaPParameters contained in the mapping relation; u represents a voltage; a. b, c, e are shown separatelyConstant impedance load, constant current load, constant power load, and coefficient of measurement noise.
And 3, identifying the voltage sensitive load model parameters by using a particle swarm algorithm. Defining the best position of each particle which is continuously searched as the individual optimal position, and marking as Pbest(ii) a The best position that all particles of the population have undergone is defined as the global optimal position, which is marked as Gbest. All particles have a fitness value determined by an optimization function, and the particles are continuously updated by 2 extreme values during iteration to generate individuals of the next generation. M particles defining a D-dimensional search space, wherein the position of the ith particle is XiThe best past position is noted as Pbest(g) At a velocity of Vi. Each particle adjusts its own velocity and position by equations (12) to (13):
wherein the content of the first and second substances,
representing the speed of the ith particle at the ith iteration; omega is an inertia weight, so that the particles can keep the motion inertia; c. C1、c2Is a learning factor, can be set to 2; rand1、rand2Is [0,1 ]]A random number in between; pbest(g) Representing the best position each particle has experienced while continuously looking for; gbest(g) Representing the best positions that all particles of the population have experienced;
indicating the position of the ith particle at the ith iteration.
The specific operation method comprises the following steps:
step 3.1, inputting voltage, active power measurement data and static loadThe parameters of the model to be identified. Setting the size of the particle group as m and the initial position xiInitial velocity viAnd limiting the variation range x of position and speedi∈[xmin,xmax]、vi∈[vmin,vmax]. Given inertia weight omega and maximum iteration number NmaxLearning factor c1、c2Initializing Pbest、Gbest。。
And 3.2, defining a fitness function of the particles as shown in the formula (14):
in the formula, N is the number of the measurement data sampling points; u shapekMeasuring the voltage quantity; pkMeasuring active power; f (-) is the function to be identified.
Step 3.3, the fitness value of the particle is compared with the best position P experiencedbestComparing, if it is better than PbestThen replace P with the current fitness valuebest(ii) a Simultaneously with the global best position G experiencedbestIn contrast, if it is better than GbestThen update GbestThe position of (a). The position and velocity of the particle are updated using equations (12) to (13). If the fitness value does not meet the set error limit or the iteration number is smaller than the upper limit, switching to the second step; otherwise, ending the network training and outputting the optimal fitness value and the corresponding model parameter identification result.
Step 4, separating out the voltage sensitive load PuThen, deducting the voltage sensitive load P from the load power PuThe voltage U is connected to a negative random component PεThe correlation between the two is taken as an evaluation function of the output result of the particle swarm optimization, wherein the correlation analysis expression is
And evaluating the effectiveness of the output result of the particle swarm algorithm. Through continuous searching of particles, the voltage U and the negative random component are foundPεThe corresponding model parameter is used as the optimal parameter identification result when the correlation between the model parameters is minimum. Obtaining a load random component PεThe method comprises the following steps:
the voltage sensitive load P is calculated by the formula (10)uThen, the load random component P is calculated using the formula (15)ε。
Pε＝P-Pu (15)
Step 5, after identifying the voltage sensitive load model parameters, calculating an active voltage characteristic coefficient pu. Working out characteristic coefficient p of active voltageuThe method comprises the following steps:
after identifying the voltage sensitive load model parameters in step 4, calculating the active voltage characteristic coefficient p by using the formula (16)u。
pu＝2×a×U+b (16)
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (5)
1. A voltage sensitive load model and a parameter identification method are characterized by comprising the following steps:
step 1, acquiring voltage and load power data of a 10/0.4kV transformer through a power grid measuring device; then, carrying out data normalization processing on the collected voltage data and load power data;
step 2, selecting an improved load model to fit a mapping relation between voltage and voltage sensitive load; building a static model of the voltage-sensitive load shown in the formula (2) based on the traditional ZIP model shown in the formula (1), so that the load is divided into a voltage-sensitive partial load and a voltage-independent load; the measurement noise is considered to be 4 parts in total, wherein the constant impedance and constant current parts are P sensitive to voltageUIs the part that needs to establish the mapping function; the residue is leftThe lower load random component and the measurement noise have no correlation with the voltage fluctuation as shown in the formula (3), so that the lower load random component and the measurement noise are taken as a whole; therefore, the improved static load model is that the constant impedance and constant current parts of the original ZIP model are reserved, and the rest parts are separated by adopting a proper method, so that the mapping relation between the voltage and the voltage sensitive load is established;
P＝aU2+bU+c+e (1)
Pu＝Fu(P,U,θP)＝a·U2+b·U (2)
Pε＝c+e (3)
in the formula: p represents the load power of the low-voltage transformer area; puThe load is a part of the load sensitive to voltage, namely the load with which the power of the load changes when the voltage fluctuates; pεRepresents a voltage independent load, i.e. a load that does not vary with voltage fluctuations; fu(. cndot.) represents a non-linear mapping function between voltage to voltage sensitive load; thetaPParameters contained in the mapping relation; u represents a voltage; a. b, c and e respectively represent coefficients of constant impedance load, constant current load, constant power load and measurement noise;
step 3, identifying voltage sensitive load model parameters by using a particle swarm algorithm; defining the best position of each particle which is continuously searched as the individual optimal position, and marking as Pbest(ii) a The best position that all particles of the population have undergone is defined as the global optimal position, which is marked as Gbest(ii) a All the particles have fitness values determined by an optimized function, and the particles are continuously updated by 2 extreme values during iteration to generate individuals of the next generation; m particles defining a D-dimensional search space, wherein the position of the ith particle is XiThe best past position is noted as Pbest(g) At a velocity of Vi(ii) a The speed and position of each particle are adjusted by the following formulas (4) to (5):
wherein the content of the first and second substances,
representing the speed of the ith particle at the ith iteration; omega is an inertia weight, so that the particles can keep the motion inertia; c. C1、c2A learning factor, set to 2; rand1、rand2Is [0,1 ]]A random number in between; pbest(g) Representing the best position each particle has experienced while continuously looking for; gbest(g) Representing the best positions that all particles of the population have experienced;
representing the position of the ith particle at the ith iteration;
step 4, separating out the voltage sensitive load PuThen, deducting the voltage sensitive load P from the load power PuThe voltage U and the load random component PεThe correlation between the two is taken as an evaluation function of the output result of the particle swarm optimization, wherein the correlation analysis expression is
Evaluating the effectiveness of the output result of the particle swarm algorithm; wherein t represents time; u (t) represents the voltage at time t;
represents the U-mean of the voltage; pε(t) represents a voltage-independent load at time t;
represents the power PεThe mean value of (a); ρ represents the voltage U and the power PεA correlation coefficient between; through continuous searching of particles, a random component P enabling the voltage U and the load to be obtained is foundεCorrelation betweenTaking the corresponding model parameter as the optimal parameter identification result when the sex is minimum;
step 5, after identifying the voltage sensitive load model parameters, calculating an active voltage characteristic coefficient pu。
2. The voltage-sensitive load model and parameter identification method according to claim 1, wherein the specific operation method for performing normalization processing on the collected voltage data and load power data in step 1 is as follows:
data normalization processing: the normalization processing formula is as follows:
wherein, Uj、PjRespectively represent the normalized voltage u at time jjPower pjPer unit value of; u. ofmax、uminAre the voltage of the day U respectively0Maximum and minimum values of pmax、pminRespectively the load power P of the day0Maximum and minimum values of.
3. The method of claim 1, wherein the specific operation of step 3 is as follows:
step 3.1, inputting voltage, active power measurement data and parameters to be identified of the static load model; setting the size of the particle group as m and the initial position xiInitial velocity viAnd limiting the variation range x of position and speedi∈[xmin,xmax]、vi∈[vmin,vmax](ii) a Given inertia weight omega and maximum iteration number NmaxLearning factor c1、c2Initializing Pbest、Gbest；
And 3.2, defining a fitness function of the particles as shown in the formula (6):
in the formula, R is the number of sampling points of the measured data; u shapekMeasuring the voltage quantity; pkMeasuring active power; f (-) is the function to be identified;
step 3.3, the fitness value of the particle is compared with the best position P experiencedbestComparing, if it is better than PbestThen replace P with the current fitness valuebest(ii) a Simultaneously with the global best position G experiencedbestIn contrast, if it is better than GbestThen update GbestThe position of (a); updating the position and the speed of the particles by adopting formulas (4) to (5); if the fitness value does not meet the set error limit or the iteration number is smaller than the upper limit, turning to the step 2; otherwise, ending the network training and outputting the optimal fitness value and the corresponding model parameter identification result.
4. The voltage-sensitive load model and parameter identification method of claim 1, wherein the load random component P is obtained in step 4εThe method comprises the following steps:
calculating the voltage sensitive load P by the formula (2)uThen, the random component P of the load is calculated by using the formula (7)ε；
Pε＝P-Pu (7)。
5. The voltage-sensitive load model and parameter identification method according to claim 1, wherein the active voltage characteristic coefficient p is obtained in step 5uThe method comprises the following steps:
after identifying the voltage sensitive load model parameters in step 4, calculating the active voltage characteristic coefficient p by using a formula (8)u；
pu＝2×a×U+b (8)。
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