CN113095564A - Power distribution network prediction auxiliary device and prediction state estimation method thereof - Google Patents

Power distribution network prediction auxiliary device and prediction state estimation method thereof Download PDF

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CN113095564A
CN113095564A CN202110374723.8A CN202110374723A CN113095564A CN 113095564 A CN113095564 A CN 113095564A CN 202110374723 A CN202110374723 A CN 202110374723A CN 113095564 A CN113095564 A CN 113095564A
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rtu
measurement
prediction
state
value
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王涛
容春艳
柴林杰
高立坡
康伟
任志刚
李军阔
郭佳
申永鹏
李光毅
唐帅
郝军魁
林榕
王中亮
孙乾
李江
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State Grid Corp of China SGCC
Northeast Electric Power University
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
Northeast Dianli University
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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Abstract

The invention provides a power distribution network prediction auxiliary device and a prediction state estimation method thereof, wherein the power distribution network prediction auxiliary device comprises the following steps: the box, the left front bolt in box top has the controller, the left back bolt in box top has the host computer, all the bolts all around at box top have the support column, the top bolt of support column has the detection platform, detect the left front in platform top and placed the keyboard, it has the display to detect the left back bolt in platform top, the bottom on display right side is from last to inserting in proper order down and being equipped with the connecting wire. According to the invention, through the matching of the through grooves, the servo motor, the rotating rod, the driving gear, the driven gear, the lead screw, the threaded sleeve, the moving seat and the universal wheel, the box body can be moved, stood and fixed according to the actual requirements of a user on site, so that the user can conveniently and rapidly perform prediction operation on power distribution network equipment, the prediction efficiency of a power distribution network is accelerated, and the long-time work waiting of the power distribution network is avoided.

Description

Power distribution network prediction auxiliary device and prediction state estimation method thereof
Technical Field
The invention relates to the field of power distribution network state estimation methods, in particular to a power distribution network prediction auxiliary device and a prediction state estimation method thereof.
Background
In a modern power grid, the power distribution network state estimation generally adopts node voltage, branch current and branch power as state variables to carry out a power distribution network state estimation algorithm, and data acquisition is obtained by real-time measurement of an SCADA (supervisory control and data acquisition) system. RTU equipment monitors and collects steady state parameters such as current effective value, active power, reactive power, power factor and the like, and is widely applied to a dispatching automation system. The mu PMU can acquire voltage and current phasors in real time, is expected to be configured in a large scale in a power distribution network in the future, and realizes wide-area protection. The mu PMU and the RTU data acquisition application system are independent, and have advantages respectively, but also have connection. Therefore, the data of the mu PMU and the RTU are deeply fused, the observability of the power distribution network is increased, and the development of high-level application software of the power distribution network has important value.
At present, the power distribution network state estimation is researched more at home and abroad, the power distribution network state estimation algorithm comprises a least square algorithm, a quick decomposition algorithm, an orthogonal transformation algorithm and a measurement transformation state estimation algorithm, the least square algorithm has the problem of weak tolerance, the quick decomposition algorithm has the problem of large error, the orthogonal transformation algorithm has the problem of large memory occupation, and the measurement transformation state estimation algorithm has the problem of low efficiency.
When a user estimates the state of the power distribution network, the power distribution network needs to be predicted and estimated by means of the prediction auxiliary device, however, the conventional prediction auxiliary device cannot be statically fixed and moved according to the actual requirements of the user in the use process, the maneuvering performance of the prediction auxiliary device is greatly reduced, the prediction estimation duration of the prediction auxiliary device is prolonged, and the prediction efficiency of the prediction auxiliary device is reduced.
Therefore, it is necessary to provide a power distribution network prediction support apparatus and a prediction state estimation method thereof to solve the above technical problems.
Disclosure of Invention
The invention provides a power distribution network prediction auxiliary device and a prediction state estimation method thereof, which solve the problems that the power distribution network state estimation is researched more at home and abroad, a power distribution network state estimation algorithm comprises a least square algorithm, a rapid decomposition algorithm, an orthogonal transformation algorithm and a measurement transformation state estimation algorithm, the least square algorithm has the problem of weak tolerance capability, the rapid decomposition algorithm has the problem of large error, the orthogonal transformation algorithm has the problem of large memory occupation, the measurement transformation state estimation algorithm has low efficiency, and the conventional prediction auxiliary device cannot be statically fixed and moved according to the actual requirements of a user in the using process.
In order to solve the above technical problem, the power distribution network prediction assisting apparatus provided by the present invention includes: the box, the left front bolt in box top has the controller, the left back bolt in box top has the host computer, all bolts all around at box top have the support column, the top bolt of support column has the detection platform, detect the left front in platform top and placed the keyboard, it has the display to detect the left back bolt in platform top, the bottom on display right side is from last to inserting in proper order down and being equipped with the connecting wire, one side that the display was kept away from to the connecting wire is inserted and is equipped with detecting instrument, detecting instrument's right side swing joint has the detection line probe after to in the past in proper order, the inner chamber of box is provided with lifting adjusting mechanism.
Preferably, the lifting adjusting mechanism comprises a through groove, a servo motor, a rotating rod, a driving gear, a driven gear, a screw rod, a threaded sleeve, a moving seat and universal wheels, the through groove is formed in the periphery of the bottom of an inner cavity of the box body, the servo motor is bolted to the top of the back of the box body, an output shaft of the servo motor penetrates through the box body and is bolted with the rotating rod, the driving gear is sequentially bolted to the surface of the rotating rod from the front to the back, the driven gear is meshed with the bottom of the driving gear, the screw rod is vertically bolted to an inner cavity of the driven gear, the bottom of the screw rod is rotatably connected with the bottom of the inner cavity of the box body, the threaded sleeve is connected to the surface of the screw rod in a threaded mode, the moving seat is bolted to the outer side of the threaded sleeve, and the universal.
Preferably, supporting legs are bolted to the periphery of the bottom of the box body and located on the outer sides of the universal wheels, and anti-skidding heads are bolted to the bottoms of the supporting legs.
Preferably, the periphery of the top of the inner cavity of the box body is vertically bolted with limiting springs, the bottoms of the limiting springs are bolted with the top of the movable seat, and the center of the bottom of the inner cavity of the box body is bolted with an anti-collision rubber pad.
Preferably, a tool box is placed on the right side of the top of the box body, a handle frame is bolted to the left side of the detection table, and a handle sleeve is sequentially sleeved on the top of the handle frame from front to back.
Preferably, the method for estimating the prediction state of the power distribution network prediction assistance device is characterized by including the following steps:
the method comprises the following steps: acquiring mu PMU and RTU measurement data, and filling the RTU measurement data by using an optimal weighted average interpolation method to realize the fusion of the mu PMU and the RTU measurement data;
step two: establishing a mixed measurement equation according to a mu PMU measurement equation and an RTU measurement equation;
step three: establishing a state transition equation by adopting a Holt's two-parameter exponential smoothing method:
step four: establishing a FASE algorithm data model for mixed measurement, and performing state equation prediction and estimation, measurement data prediction and filtering correction by using a volume Kalman filtering technology;
step five: the validity of the verification method is verified by adopting an IEEE 37 node system, the verification system is an IEEE 37 node system, and system parameters are as follows: the voltage level is 4.8KV, the power reference value is 1MVA, and the rated capacities of the photovoltaic power generation systems PV1 and PV2 are 0.2MW and are respectively connected to the nodes 704 and 708. In fig. 4, the measurement nodes of PMU and RTU are marked with blue rectangles and red triangles, respectively.
Preferably, in the first step, in a power frequency 50Hz power grid, the update cycle of the RTU data is generally 1s, the update cycle of the μ PMU measurement data is 20 to 200ms, and the update cycle of the RTU is long, so that the RTU data needs to be filled according to the μ PMU data and the RTU historical data, and the data filling method adopts optimal weighted average interpolation, that is, the data between two sample points is calculated by weighting the linear interpolation value and the historical average interpolation value of the RTU;
Figure BDA0003010821160000031
Figure BDA0003010821160000032
Figure BDA0003010821160000033
in the formula xRTUhMeasured value of the previous time, xRTUjIn order to measure the value at the later moment,
Figure BDA0003010821160000034
linear interpolation of value for i time
Figure BDA0003010821160000035
The average interpolation value of the history at the moment i, H, alpha, the weight parameter and the typical value of 0.108 and d are takeniIs the distance from the estimate to the nearest sample that is accessible.
Preferably, during step two, the mu PMU measurement node quantity data is presented in polar coordinates. Converting voltage current phasors U & lt theta & gt and I & lt theta & gt in a coordinate form into a rectangular coordinate variable:
Figure BDA0003010821160000041
where V and/represent the voltage and current magnitudes measured by the μ PMU; thetaV，θIIs the measured voltage and current phase angle; vr (P)，Vx(P)Respectively a real part and an imaginary part of the voltage phasor after coordinate transformation; i isr(P)，Ix(P)Representing the real part and the imaginary part of the current phasor after coordinate transformation;
establishing an RTU measurement equation: for the three-phase power measured by the RTU, the active power P and the reactive power Q are expressed as:
P＝VRTUIRTU cos(φRTU) (5)
Q＝VRTUIRTU sin(φRTU) (6)
in the formula: vRTUAnd IRTURespectively representing the voltage amplitude and the injection current amplitude; cos (phi)RTU) Is the power factor. Power can also be represented by the real and imaginary parts of voltage and current:
P＝VRIR+VIII (7)
Q＝-VRII+VIIR (8)
converting the RTU measurement to an equivalent injection current:
Figure BDA0003010821160000042
Figure BDA0003010821160000043
in the formula: i isr(eq)、Ix(eq)Representing the real and imaginary parts of the equivalent injected current.
For current magnitude measurements from the RTU, to reduce errors due to phase angle information, the square of the branch current magnitude is used as the equivalent branch current measurement:
Ieq＝(IRTU)2(11)
and equivalently converting the three-phase node voltage amplitude measurement of the RTU into:
Figure BDA0003010821160000051
in the formula: i VRTUL is the voltage amplitude of the three-phase node; vcalIs a three-phase node voltage phasor calculated based on the branch current; vr(eq)，Vx(eq)The real part and the imaginary part of the three-phase node voltage after equivalent transformation;
establishing a mixed measurement equation: the voltage and current phasor measurement data are converted through coordinates, and the mixed measurement variable Z is obtained under a rectangular coordinate systemMComprises the following steps:
Figure BDA0003010821160000052
the specific measurement data are as follows:
Figure BDA0003010821160000053
in the formula:
Figure BDA0003010821160000055
representing a node voltage phasor;
Figure BDA0003010821160000056
is a branch current phasor measurement value;
Figure BDA0003010821160000057
is an injected current phasor measurement value; the corner mark P represents the data source as mu PMU measurement value; eq indicates that the data source is an RTU measurement. According to the mixed measurement system, the mixed measurement equation hM(g) The specific expression of (A) is as follows:
Figure BDA0003010821160000054
preferably, during the step three, the state transition equation is established by first defining a state variable:
Figure BDA0003010821160000061
wherein x is an n-dimensional state variable; l represents A, B, C phases in the distribution network;
Figure BDA0003010821160000062
are the real and imaginary parts of the three-phase branch current.
Holt's two-parameter exponential smoothing method is used for writing a state transition equation. The method uses two smoothing parameters α and β. Holt's two-parameter linear exponential smoothing model[19]Is shown in equation (18).
Figure BDA0003010821160000063
In the formula:
Figure BDA0003010821160000064
the variable prediction value at the t +1 moment obtained by using the state transition matrix at the t moment is shown;
Figure BDA0003010821160000065
is the state value at time t; m istAnd ntRepresenting horizontal and vertical components, respectively; alpha is alphaHAnd betaHFor two smoothing parameters, alphaHIs the weight of the impact of recent data on the future; beta is aHRepresenting the trend of the supplemental data lag.
Preferably, during the step four, the volume point is calculated by using the state variable estimation result and the error covariance matrix at the previous moment:
Figure BDA0003010821160000066
in the formula: t is t-Is the instant before the new measurement value arrives; t is the instant a new measurement arrives; x is the number oft-1|t-1Is based on
Figure BDA0003010821160000067
Calculating a volume point of the ith state variable by using the state estimation value;
Figure BDA0003010821160000068
is the posterior probability distribution at time t-1. Wherein P ist-1|t-1Obtained by the formula (20).
Figure BDA0003010821160000069
In the formula:
Figure BDA00030108211600000610
is the state prediction value at time t-;
Figure BDA00030108211600000611
is a state prediction error covariance matrix, which is an n-dimensional diagonal matrix; qt-1Is the noise error covariance at time t-1.
The state prediction value volume points of equal weight are propagated through the state equation as shown in equation (21):
Figure BDA00030108211600000612
in the formula:
Figure BDA0003010821160000071
is the volume point after propagation through the equation of state at time t. The value is obtained by weighted summation.
Figure BDA0003010821160000072
And calculating the volume point predicted by the equal weight measurement by using the state prediction value obtained in the state prediction step:
Figure BDA0003010821160000073
in the formula:
Figure BDA00030108211600000712
is the volume point for each measured subvector at time t through the measurement function h (g). And (3) measuring and predicting value:
Figure BDA0003010821160000074
predicting a covariance matrix based on the results of the state prediction and metrology prediction steps
Figure BDA0003010821160000075
And cross covariance matrix between state variable x and quantity measurement z
Figure BDA0003010821160000076
Figure BDA0003010821160000077
Figure BDA0003010821160000078
Measurement of state variables by time ttFiltering and correcting, and calculating Kalman gain K by Kalman filtering methodt. And state estimation result
Figure BDA0003010821160000079
And correlation error covariance Pt|tThe calculation of (2):
Figure BDA00030108211600000710
Figure BDA00030108211600000711
Figure BDA0003010821160000081
to quantify the estimation error of the proposed algorithm, the estimation performance function is defined in (30) and (31):
Figure BDA0003010821160000082
Figure BDA0003010821160000083
in the formula:
Figure BDA0003010821160000084
xi，tdenotes the ith time tthEstimated values and true values of the individual state variables; δ is the absolute error of the state variable estimate; epsilontRepresents the estimated root mean square error at the time instant; n is the dimension of the state variable.
A composite error function defining the overall estimation performance of the sampling system:
Figure BDA0003010821160000085
in the formula: ε is the root mean square error of the system; t is the total number of samples of the simulation.
Compared with the related art, the power distribution network prediction auxiliary device and the prediction state estimation method thereof provided by the invention have the following beneficial effects:
the invention provides a power distribution network prediction auxiliary device and a prediction state estimation method thereof,
1. the invention can be matched with the universal wheel through the through groove, the servo motor, the rotating rod, the driving gear, the driven gear, the screw rod, the threaded sleeve, the moving seat and the universal wheel according to the actual requirements of a user on site, the box body is moved and kept still for fixing, so that a user can conveniently and quickly predict the power distribution network equipment, the prediction efficiency of the power distribution network is improved, the long-time work waiting of the power distribution network is avoided, through the cooperation of the procedures of the first step, the second step, the third step, the fourth step and the fifth step, can perform accurate formula calculation for mu PMU measurement, RTU measurement equation and mixed measurement equation, the correct solutions of the state transition equation and the state prediction and correction equation can be obtained, and the problems that the least square algorithm in the traditional prediction estimation method is weak in tolerance, large in fast decomposition algorithm error, large in memory occupation of the orthogonal transformation algorithm and low in efficiency of the measurement transformation state estimation algorithm are solved;
2. the bottom of the box body can be stably supported at the periphery in a standing and fixing state through the supporting legs, the stability of the box body in the standing process is improved, the friction coefficient between the supporting legs and the ground surface is increased through the anti-slip heads, the supporting legs are prevented from sliding and displacing on the ground surface, the ascending height of the movable seat can be elastically limited through the limiting springs, collision between the movable seat and the driven gear is avoided, the descending height of the movable seat can be prevented from being subjected to collision treatment through the anti-collision rubber pads, the movable seat and the bottom of the inner cavity of the box body are prevented from being collided, a user can conveniently place required tools and other articles through the tool box, the user can conveniently push the box body and the detection table through the hand pushing frame and the handle sleeve, and the quick mobility of the prediction auxiliary device is.
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Fig. 1 is a schematic structural diagram of a power distribution network prediction assisting apparatus and a prediction state estimation method thereof according to a preferred embodiment of the present invention;
fig. 2 is a rear view of the power distribution network prediction assistance apparatus shown in fig. 1 and a prediction state estimation method thereof;
FIG. 3 is a sectional view of the structure of the case shown in FIG. 1;
fig. 4 is a graph showing a relationship between an update cycle of RTU and μ PMU data of the power distribution network prediction assistance apparatus and the prediction state estimation method shown in fig. 1;
FIG. 5 is a schematic diagram of data mixing preprocessing of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in FIG. 1;
fig. 6 is a flowchart of a prediction assistance state estimation algorithm of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1;
FIG. 7 is a schematic diagram of an IEEE 37 node system of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in FIG. 1;
fig. 8 is a state estimation diagram of branch currents of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1;
fig. 9 is a state estimation diagram of branch currents of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1.
Reference numbers in the figures: 1. a box body; 2. a controller; 3. a host; 4. a support pillar; 5. a detection table; 6. a keyboard; 7. a display; 8. a connecting wire; 9. detecting an instrument; 10. detecting a line probe; 11. a lifting adjusting mechanism; 111. a through groove; 112. a servo motor; 113. a rotating rod; 114. a driving gear; 115. a driven gear; 116. a screw rod; 117. a threaded sleeve; 118. a movable seat; 119. a universal wheel; 12. supporting legs; 13. a limiting spring; 14. a tool box; 15. a hand pushing frame.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Please refer to fig. 1, fig. 2, fig. 3, fig. 4, fig. 5, fig. 6, fig. 7, fig. 8, and fig. 9 in combination, wherein fig. 1 is a schematic structural diagram of a power distribution network prediction assistance device and a prediction state estimation method thereof according to a preferred embodiment of the present invention; fig. 2 is a rear view of the power distribution network prediction assistance apparatus shown in fig. 1 and a prediction state estimation method thereof; FIG. 3 is a sectional view of the structure of the case shown in FIG. 1; fig. 4 is a graph showing a relationship between an update cycle of RTU and μ PMU data of the power distribution network prediction assistance apparatus and the prediction state estimation method shown in fig. 1; FIG. 5 is a schematic diagram of data mixing preprocessing of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in FIG. 1; fig. 6 is a flowchart of a prediction assistance state estimation algorithm of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1; FIG. 7 is a schematic diagram of an IEEE 37 node system of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in FIG. 1; fig. 8 is a state estimation diagram of branch currents of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1; fig. 9 is a state estimation diagram of branch currents of the power distribution network prediction assistance apparatus and the prediction state estimation method thereof shown in fig. 1. Distribution network prediction auxiliary device includes: box 1, the left front bolt in box 1 top has been met controller 2, the left back bolt in box 1 top has been met host computer 3, all bolt connections all around at box 1 top have support column 4, the top bolt of support column 4 has been met and is examined test table 5, examine the left front in test table 5 top and placed keyboard 6, examine the left back bolt in test table 5 top and connect display 7, the bottom on display 7 right side is from last to inserting down in proper order and being equipped with connecting wire 8, connecting wire 8 is kept away from one side of display 7 and is inserted and be equipped with detecting instrument 9, detecting instrument 9's right side from the past to back swing joint has detection line probe 10 in proper order, the inner chamber of box 1 is provided with lifting adjusting mechanism 11.
The lifting adjusting mechanism 11 comprises a through groove 111, a servo motor 112, a rotating rod 113, a driving gear 114, a driven gear 115, a screw rod 116, a threaded sleeve 117, a moving seat 118 and a universal wheel 119, the through groove 111 is formed around the bottom of an inner cavity of the box body 1, the servo motor 112 is bolted to the top of the back of the box body 1, an output shaft of the servo motor 112 penetrates through the box body 1 and is bolted with the rotating rod 113, the driving gear 114 is sequentially bolted to the surface of the rotating rod 113 from front to back, the driven gear 115 is meshed to the bottom of the driving gear 114, the screw rod 116 is vertically bolted to the inner cavity of the driven gear 115, the bottom of the screw rod 116 is rotatably connected to the bottom of the inner cavity of the box body 1, the threaded sleeve 117 is connected to the surface of the screw rod 116 in a threaded manner, the moving seat 118 is bolted to the outer side of the threaded sleeve 117, and the universal wheel 119 matched, .
The periphery of the bottom of the box body 1 is bolted with supporting legs 12, the supporting legs 12 are positioned at the outer side of the universal wheels 119, and the bottom of the supporting legs 12 is bolted with anti-skid heads.
The periphery of the top of the inner cavity of the box body 1 is vertically bolted with a limiting spring 13, the bottom of the limiting spring 13 is bolted with the top of the movable seat 118, and the center of the bottom of the inner cavity of the box body 1 is bolted with an anti-collision rubber pad.
A tool box 14 is arranged on the right side of the top of the box body 1, a push hand frame 15 is bolted on the left side of the detection table 5, and gloves are sequentially sleeved on the top of the push hand frame 15 from front to back.
A prediction state estimation method of a power distribution network prediction auxiliary device is characterized by comprising the following steps:
the method comprises the following steps: acquiring mu PMU and RTU measurement data, and filling the RTU measurement data by using an optimal weighted average interpolation method to realize the fusion of the mu PMU and the RTU measurement data;
step two: establishing a mixed measurement equation according to a mu PMU measurement equation and an RTU measurement equation;
step three: establishing a state transition equation by adopting a Holt's two-parameter exponential smoothing method:
step four: establishing a FASE algorithm data model for mixed measurement, and performing state equation prediction and estimation, measurement data prediction and filtering correction by using a volume Kalman filtering technology;
step five: the validity of the verification method is verified by adopting an IEEE 37 node system, the verification system is an IEEE 37 node system, and system parameters are as follows: the voltage level is 4.8KV, the power reference value is 1MVA, and the rated capacities of the photovoltaic power generation systems PV1 and PV2 are 0.2MW and are respectively connected to the nodes 704 and 708. In fig. 4, the measurement nodes of PMU and RTU are marked with blue rectangles and red triangles, respectively.
In the first step, in a power frequency 50Hz power grid, the updating period of RTU data is generally 1s, the updating period of mu PMU measurement data is 20-200 ms, the updating period of the RTU is longer, so that the RTU data needs to be filled according to the mu PMU data and RTU historical data, and the data filling method adopts optimal weighted average interpolation, namely, the data between two sample points is calculated by weighting the linear interpolation value and the historical average interpolation value of the RTU;
Figure BDA0003010821160000111
Figure BDA0003010821160000112
Figure BDA0003010821160000121
in the formula xRTUhMeasured value of the previous time, xRTUjIn order to measure the value at the later moment,
Figure BDA0003010821160000122
linear interpolation of value for i time
Figure BDA0003010821160000123
The average interpolation value of the history at the moment i, H, alpha, the weight parameter and the typical value of 0.108 and d are takeniIs the distance from the estimate to the nearest sample that is accessible.
In the second step, the data of the mu PMU measurement node is presented in a polar coordinate form. Converting voltage current phasors U & lt theta & gt and I & lt theta & gt in a coordinate form into a rectangular coordinate variable:
Figure BDA0003010821160000124
wherein V and I represent the voltage and current amplitudes measured by the mu PMU; thetaV，θIIs the measured voltage and current phase angle; vr (P)，Vx(P)Respectively a real part and an imaginary part of the voltage phasor after coordinate transformation; i isr(P)，Ix(P)Representing the real part and the imaginary part of the current phasor after coordinate transformation;
establishing an RTU measurement equation: for the three-phase power measured by the RTU, the active power P and the reactive power Q are expressed as:
P＝VRTUIRTU cos(φRTU) (5)
Q＝VRTUIRTU sin(φRTU) (6)
in the formula: vRTUAnd IRTURespectively representing the voltage amplitude and the injection current amplitude; cos (phi)RTU) Is the power factor. Power can also be represented by the real and imaginary parts of voltage and current:
P＝VRIR+VIII (7)
Q＝-VRII+VIIR (8)
converting the RTU measurement to an equivalent injection current:
Figure BDA0003010821160000125
Figure BDA0003010821160000126
in the formula: i isr(eq)、Ix(eq)Representing the real and imaginary parts of the equivalent injected current.
For current magnitude measurements from the RTU, to reduce errors due to phase angle information, the square of the branch current magnitude is used as the equivalent branch current measurement:
Ieq＝(IRTU)2 (11)
and equivalently converting the three-phase node voltage amplitude measurement of the RTU into:
Figure BDA0003010821160000131
in the formula: i VRTUL is the voltage amplitude of the three-phase node; vcalIs a three-phase node voltage phasor calculated based on the branch current; vr(eq)，Vx(eq)Is the real of the equivalent transformation of the three-phase node voltageA partial and an imaginary part;
establishing a mixed measurement equation: the voltage and current phasor measurement data are converted through coordinates, and the mixed measurement variable Z is obtained under a rectangular coordinate systemMComprises the following steps:
Figure BDA0003010821160000132
the specific measurement data are as follows:
Figure BDA0003010821160000133
in the formula:
Figure BDA0003010821160000134
representing a node voltage phasor;
Figure BDA0003010821160000135
is a branch current phasor measurement value;
Figure BDA0003010821160000136
is an injected current phasor measurement value; the corner mark P represents the data source as mu PMU measurement value; eq indicates that the data source is an RTU measurement. According to the mixed measurement system, the mixed measurement equation hM(g) The specific expression of (A) is as follows:
Figure BDA0003010821160000141
in the third step, a state transition equation is established, and firstly, a state variable is defined:
Figure BDA0003010821160000142
wherein x is an n-dimensional state variable; l represents A, B, C phases in the distribution network;
Figure BDA0003010821160000143
are the real and imaginary parts of the three-phase branch current.
Holt's two-parameter exponential smoothing method is used for writing a state transition equation. The method uses two smoothing parameters a and β. Holt's two-parameter linear exponential smoothing model[19]Is shown in equation (18).
Figure BDA0003010821160000144
In the formula:
Figure BDA0003010821160000145
the variable prediction value at the t +1 moment obtained by using the state transition matrix at the t moment is shown;
Figure BDA0003010821160000146
is the state value at time t; m istAnd ntRepresenting horizontal and vertical components, respectively; alpha is alphaHAnd betaHFor two smoothing parameters, alphaHIs the weight of the impact of recent data on the future; beta is aHRepresenting the trend of the supplemental data lag.
In the fourth step, the volume point is calculated by using the state variable estimation result and the error covariance matrix at the known previous moment:
Figure BDA0003010821160000151
in the formula: t is t-Is the instant before the new measurement value arrives; t is the instant a new measurement arrives; x is the number oft-1|t-1Is based on
Figure BDA0003010821160000152
Calculating a volume point of the ith state variable by using the state estimation value;
Figure BDA0003010821160000153
is the posterior probability distribution at time t-1. Wherein P ist-1|t-1Obtained by the formula (20).
Figure BDA0003010821160000154
In the formula:
Figure BDA0003010821160000155
is the state prediction value at time t-;
Figure BDA0003010821160000156
is a state prediction error covariance matrix, which is an n-dimensional diagonal matrix; qt-1Is the noise error covariance at time t-1.
The state prediction value volume points of equal weight are propagated through the state equation as shown in equation (21):
Figure BDA0003010821160000157
in the formula:
Figure BDA0003010821160000158
is the volume point after propagation through the equation of state at time t. The value is obtained by weighted summation.
Figure BDA0003010821160000159
And calculating the volume point predicted by the equal weight measurement by using the state prediction value obtained in the state prediction step:
Figure BDA00030108211600001510
in the formula:
Figure BDA00030108211600001511
is the volume point for each measured subvector at time t through the measurement function h (g). And (3) measuring and predicting value:
Figure BDA00030108211600001512
predicting a covariance matrix based on the results of the state prediction and metrology prediction steps
Figure BDA00030108211600001513
And cross covariance matrix between state variable x and quantity measurement z
Figure BDA00030108211600001514
Figure BDA0003010821160000161
Figure BDA0003010821160000162
Measurement of state variables by time ttFiltering and correcting, and calculating Kalman gain K by Kalman filtering methodt. And state estimation result
Figure BDA0003010821160000163
And correlation error covariance Pt|tThe calculation of (2):
Figure BDA0003010821160000164
Figure BDA0003010821160000165
Figure BDA0003010821160000166
to quantify the estimation error of the proposed algorithm, the estimation performance function is defined in (30) and (31):
Figure BDA0003010821160000167
Figure BDA0003010821160000168
in the formula:
Figure BDA0003010821160000169
xi，tdenotes the ith time tthEstimated values and true values of the individual state variables; δ is the absolute error of the state variable estimate; epsilontRepresents the estimated root mean square error at the time instant; n is the dimension of the state variable.
A composite error function defining the overall estimation performance of the sampling system:
Figure BDA00030108211600001610
in the formula: ε is the root mean square error of the system; t is the total number of samples of the simulation.
The working principle of the power distribution network prediction auxiliary device and the prediction state estimation method thereof provided by the invention is as follows:
when a user needs to move the box body 1 and the detection platform 5, the controller 2 controls the servo motor 112 to drive the rotating rod 113 to rotate forward, the rotating rod 113 drives the two driving gears 114 to rotate synchronously, the two driving gears 114 drive the two screw rods 116 to rotate synchronously through the two driven gears 115, the two screw rods 116 drive the moving seat 118 to move downwards through the two thread sleeves 117 synchronously, the moving seat 118 drives the four universal wheels 119 to penetrate through the through groove 111 and move downwards, meanwhile, the box body 1 and the detection platform 5 are jacked up along with the downward movement of the four universal wheels 119, so that the supporting legs 12 are separated from the ground surface, then the user holds the hand pushing frame 15 to move the box body 1 and the detection platform 5 to a power distribution network prediction position, then the user inserts the connecting wire 8 into the display 7 and the detection instrument 9 socket, and then inserts the detection wire probe 10 into the power distribution network equipment socket, the detection instrument 9 detects and analyzes the prediction information of the power distribution network equipment, and then the prediction information is displayed by the display 7;
when the user need be to box 1 and examine test table 5 and stew fixedly, then controller 2 control servo motor 112 drives bull stick 113 and reverses, and on the same hand, remove seat 118 and drive four universal wheels 119 rebound to box 1 in, then according to box 1 and the gravity influence of examining test table 5 self, then it is fixed that box 1 drives laminating between four supporting legss 12 and the earth's surface to it is fixed to carry out steadily to support to box 1 and examine test table 5.
Compared with the related art, the power distribution network prediction auxiliary device and the prediction state estimation method thereof provided by the invention have the following beneficial effects:
according to the invention, through the matching of the through groove 111, the servo motor 112, the rotating rod 113, the driving gear 114, the driven gear 115, the lead screw 116, the threaded sleeve 117, the moving seat 118 and the universal wheel 119, the box body 1 can be moved and placed still and fixed according to the actual requirements of a user on site, so that the user can conveniently and quickly perform prediction operation on power distribution network equipment, the prediction efficiency of the power distribution network is accelerated, and long-time work waiting of the power distribution network is avoided To give a title.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A power distribution network prediction assistance device, characterized by comprising: a box body (1), a controller (2) is bolted on the front surface of the left side of the top of the box body (1), the back of the left side of the top of the box body (1) is bolted with a host (3), the periphery of the top of the box body (1) is bolted with supporting columns (4), the top of the supporting column (4) is bolted with a detection table (5), a keyboard (6) is placed on the front side of the left side of the top of the detection table (5), a display (7) is bolted on the back surface of the left side of the top of the detection table (5), connecting wires (8) are sequentially inserted from top to bottom at the bottom of the right side of the display (7), a detection instrument (9) is inserted into one side of the connecting wire (8) far away from the display (7), the right side of the detection instrument (9) is sequentially and movably connected with a detection line probe (10) from front to back, and the inner cavity of the box body (1) is provided with a lifting adjusting mechanism (11).
2. The power distribution network prediction auxiliary device according to claim 1, wherein the lifting adjusting mechanism (11) comprises a through groove (111), a servo motor (112), a rotating rod (113), a driving gear (114), a driven gear (115), a screw rod (116), a threaded sleeve (117), a moving seat (118) and a universal wheel (119), the through groove (111) is formed in the periphery of the bottom of the inner cavity of the box body (1), the servo motor (112) is bolted to the top of the back of the box body (1), an output shaft of the servo motor (112) penetrates through the box body (1) and is bolted to the rotating rod (113), the driving gear (114) is sequentially bolted to the surface of the rotating rod (113) from front to back, the bottom of the driving gear (114) is engaged with the driven gear (115), the inner cavity of the driven gear (115) is vertically bolted to the screw rod (116), and the bottom of the screw rod (116) is rotatably connected to the bottom of the inner cavity of the box body (, the surface threaded connection of lead screw (116) has thread bush (117), the outside bolt joint of thread bush (117) has removed seat (118), all bolt joints all around removing seat (118) bottom have with logical groove (111) cooperation universal wheel (119) of using.
3. The power distribution network prediction auxiliary device according to claim 1, characterized in that supporting feet (12) are bolted to the periphery of the bottom of the box body (1), the supporting feet (12) are located on the outer sides of universal wheels (119), and the bottoms of the supporting feet (12) are bolted with anti-skidding heads.
4. The power distribution network prediction auxiliary device according to claim 1, characterized in that a limiting spring (13) is vertically bolted on the periphery of the top of the inner cavity of the box body (1), the bottom of the limiting spring (13) is bolted on the top of the movable base (118), and an anti-collision rubber pad is bolted on the center of the bottom of the inner cavity of the box body (1).
5. The power distribution network prediction auxiliary device according to claim 1, characterized in that a tool box (14) is placed on the right side of the top of the box body (1), a handle frame (15) is bolted on the left side of the detection table (5), and a handle sleeve is sequentially sleeved on the top of the handle frame (15) from front to back.
6. The method for estimating the prediction state of the power distribution network prediction assisting device according to claim 1, characterized by comprising the following steps:
the method comprises the following steps: acquiring mu PMU and RTU measurement data, and filling the RTU measurement data by using an optimal weighted average interpolation method to realize the fusion of the mu PMU and the RTU measurement data;
step two: establishing a mixed measurement equation according to a mu PMU measurement equation and an RTU measurement equation;
step three: establishing a state transition equation by adopting a Holt's two-parameter exponential smoothing method:
step four: establishing a FASE algorithm data model for mixed measurement, and performing state equation prediction and estimation, measurement data prediction and filtering correction by using a volume Kalman filtering technology;
step five: the validity of the method is verified by adopting an lEEE 37 node system, the verification system is an IEEE 37 node system, and system parameters are as follows: the voltage level is 4.8KV, the power reference value is 1MVA, and the rated capacities of the photovoltaic power generation systems PV1 and PV2 are 0.2MW and are respectively connected to the nodes 704 and 708. In fig. 4, the measurement nodes of PMU and RTU are marked with blue rectangles and red triangles, respectively.
7. The prediction state estimation method of the power distribution network prediction auxiliary device according to claim 1, characterized in that in the first step, in a power frequency 50Hz power grid, the update period of RTU data is generally 1s, the update period of μ PMU measurement data is 20-200 ms, the update period of RTU is long, and therefore, the RTU data needs to be filled according to μ PMU data and RTU history data, and the data filling method adopts optimal weighted average interpolation, that is, the data between two sample points is calculated by weighting the linear interpolation value and the historical average interpolation value of RTU;
Figure FDA0003010821150000021
Figure FDA0003010821150000022
Figure FDA0003010821150000023
in the formula xRTUhMeasured value of the previous time, xRTUjIn order to measure the value at the later moment,
Figure FDA0003010821150000024
linear interpolation of value for i time
Figure FDA0003010821150000025
The average interpolation value of the history at the moment i, H, alpha, the weight parameter and the typical value of 0.108 and d are takeniIs the distance from the estimate to the nearest sample that is accessible.
8. The method according to claim 1, wherein the amount data of the PMU measurement node is presented in polar coordinates during step two. Converting voltage current phasors U & lt theta & gt and I & lt theta & gt in a coordinate form into a rectangular coordinate variable:
Figure FDA0003010821150000031
where V and/represent the voltage and current magnitudes measured by the μ PMU; thetaV，θIIs the measured voltage and current phase angle; vr(P)，Vx (P)Respectively a real part and an imaginary part of the voltage phasor after coordinate transformation; i isr(P)，Ix(P)Representing the real part and the imaginary part of the current phasor after coordinate transformation;
establishing an RTU measurement equation: for the three-phase power measured by the RTU, the active power P and the reactive power Q are expressed as:
P＝VRTUIRTUcos(φRTU) (5)
Q＝VRTUIRTUsin(φRTU) (6)
in the formula: vRTUAnd IRTURespectively representing the voltage amplitude and the injection current amplitude; cos (phi)RTU) Is the power factor. Power can also be represented by the real and imaginary parts of voltage and current:
P＝VRIR+VIII (7)
Q＝-VRII+VIIR (8)
converting the RTU measurement to an equivalent injection current:
Figure FDA0003010821150000032
Figure FDA0003010821150000033
in the formula: i isr(eq)、Ix(eq)Representing the real and imaginary parts of the equivalent injected current.
For current magnitude measurements from the RTU, to reduce errors due to phase angle information, the square of the branch current magnitude is used as the equivalent branch current measurement:
Ieq＝(IRTU)2 (11)
and equivalently converting the three-phase node voltage amplitude measurement of the RTU into:
Figure FDA0003010821150000041
in the formula: i VRTUL is the voltage amplitude of the three-phase node; vcalIs a three-phase node voltage phasor calculated based on the branch current; vr (eq)，Vx(eq)The real part and the imaginary part of the three-phase node voltage after equivalent transformation;
establishing a mixed measurement equation: the voltage and current phasor measurement data are converted through coordinates, and the mixed measurement variable Z is obtained under a rectangular coordinate systemMComprises the following steps:
Figure FDA0003010821150000042
the specific measurement data are as follows:
Figure FDA0003010821150000043
in the formula:
Figure FDA0003010821150000044
representing a node voltage phasor;
Figure FDA0003010821150000045
is a branch current phasor measurement value;
Figure FDA0003010821150000046
is an injected current phasor measurement value; the corner mark P represents the data source as mu PMU measurement value; eq indicates that the data source is an RTU measurement. According to the mixed measurement system, the mixed measurement equation hM(g) The specific expression of (A) is as follows:
Figure FDA0003010821150000051
9. the method according to claim 1, wherein during the third step, the state transition equation is established by first defining a state variable:
Figure FDA0003010821150000052
wherein x is an n-dimensional state variable; l represents A, B, C phases in the distribution network;
Figure FDA0003010821150000053
are the real and imaginary parts of the three-phase branch current.
Holt's two-parameter exponential smoothing method is used for writing a state transition equation. The method uses two smoothing parameters α and β. Holt's two-parameter linear exponential smoothing model[19]Is shown in equation (18).
Figure FDA0003010821150000054
In the formula:
Figure FDA0003010821150000055
the variable prediction value at the t +1 moment obtained by using the state transition matrix at the t moment is shown;
Figure FDA0003010821150000056
is the state value at time t; m istAnd ntRepresenting horizontal and vertical components, respectively; alpha is alphaHAnd betaHFor two smoothing parameters, alphaHIs the weight of the impact of recent data on the future; beta is aHRepresentsThe trend of the supplementary data lag is being developed.
10. The method according to claim 1, wherein in the step four, the volume point is calculated by using the state variable estimation result and the error covariance matrix at the previous known time:
Figure FDA0003010821150000061
in the formula: t is t-Is the instant before the new measurement value arrives; t is the instant a new measurement arrives; x is the number oft-1|t-1Is based on
Figure FDA0003010821150000062
Calculating a volume point of the ith state variable by using the state estimation value;
Figure FDA0003010821150000063
is the posterior probability distribution at time t-1. Wherein P ist-1|t-1Obtained by the formula (20).
Figure FDA0003010821150000064
In the formula:
Figure FDA0003010821150000065
is the state prediction value at time t-;
Figure FDA00030108211500000611
is a state prediction error covariance matrix, which is an n-dimensional diagonal matrix; qt-1Is the noise error covariance at time t-1.
The state prediction value volume points of equal weight are propagated through the state equation as shown in equation (21):
Figure FDA0003010821150000066
in the formula:
Figure FDA0003010821150000067
is the volume point after propagation through the equation of state at time t. The value is obtained by weighted summation.
Figure FDA0003010821150000068
And calculating the volume point predicted by the equal weight measurement by using the state prediction value obtained in the state prediction step:
Figure FDA0003010821150000069
in the formula:
Figure FDA00030108211500000610
is the volume point for each measured subvector at time t through the measurement function h (g). And (3) measuring and predicting value:
Figure FDA0003010821150000071
predicting a covariance matrix based on the results of the state prediction and metrology prediction steps
Figure FDA0003010821150000072
And cross covariance matrix between state variable x and quantity measurement z
Figure FDA0003010821150000073
Figure FDA0003010821150000074
Figure FDA0003010821150000075
Measurement of state variables by time ttFiltering and correcting, and calculating Kalman gain K by Kalman filtering methodt. And state estimation result
Figure FDA00030108211500000712
And correlation error covariance Pt|tThe calculation of (2):
Figure FDA0003010821150000076
Figure FDA0003010821150000077
Figure FDA0003010821150000078
to quantify the estimation error of the proposed algorithm, the estimation performance function is defined in (30) and (31):
Figure FDA0003010821150000079
Figure FDA00030108211500000710
in the formula:
Figure FDA00030108211500000711
xi，tdenotes the ith time tthOf a state variableAn estimated value and a true value; δ is the absolute error of the state variable estimate; epsilontRepresents the estimated root mean square error at the time instant; n is the dimension of the state variable.
A composite error function defining the overall estimation performance of the sampling system:
Figure FDA0003010821150000081
in the formula: ε is the root mean square error of the system; t is the total number of samples of the simulation.
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Application publication date: 20210709