CN112083299A - Direct current system insulation fault prediction method based on Kalman filtering - Google Patents

Direct current system insulation fault prediction method based on Kalman filtering Download PDF

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CN112083299A
CN112083299A CN202010956515.4A CN202010956515A CN112083299A CN 112083299 A CN112083299 A CN 112083299A CN 202010956515 A CN202010956515 A CN 202010956515A CN 112083299 A CN112083299 A CN 112083299A
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insulation resistance
direct current
insulation
value
kalman filtering
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CN112083299B (en
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张开迪
李世勉
梁瑜
冯宗琮
王毅
董其宇
冯波
冉小康
徐溦
叶松
刘人豪
于佩
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State Grid Corp of China SGCC
Chongqing University of Technology
Beibei Power Supply Co of State Grid Chongqing Electric Power Co Ltd
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State Grid Corp of China SGCC
Chongqing University of Technology
Beibei Power Supply Co of State Grid Chongqing Electric Power Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
    • G01R27/02—Measuring real or complex resistance, reactance, impedance, or other two-pole characteristics derived therefrom, e.g. time constant
    • G01R27/025—Measuring very high resistances, e.g. isolation resistances, i.e. megohm-meters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

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Abstract

A direct current system insulation fault prediction method based on Kalman filtering comprises the steps of firstly obtaining insulation resistance values of a direct current bus to the ground, and establishing a database of the insulation resistance of the bus to the ground; calculating the average insulation resistance of the current database
Figure DDA0002678777960000011
And average insulation resistance
Figure DDA0002678777960000012
Average insulation resistance from the next moment
Figure DDA0002678777960000013
Resistance difference value DeltaR ofiI ∈ [1, N); calculating the resistance difference value DeltaRiAnd corresponding detection interval time DeltaTiRatio X ofi(ii) a Analysis of ratio coefficient variation using Kalman filteringTrend and predicting a ratio coefficient X at the next moment; calculating the insulation resistance increase value delta R at the next moment as X multiplied by T; calculating to obtain the predicted insulation resistance value R ═ R at the next momentn+ Δ R; in the formula: rnT is the time interval between the predicted insulation resistance value R and the previous time. The invention can predict the insulation resistance at the next moment according to the historical data of the insulation resistance, thereby realizing the prepositioning of the system fault point, being beneficial to improving the positioning precision and efficiency and the like.

Description

Direct current system insulation fault prediction method based on Kalman filtering
Technical Field
The invention relates to the technical field of power system detection, in particular to a direct current system insulation fault prediction method based on Kalman filtering.
Background
The dc system is an important component of an electric power system, is widely used in power plants, substations and other places, and has a huge branch network, and is used to supply dc loads such as relay protection, control, signals, computer monitoring, emergency lighting, ac uninterruptible power supplies, and the like. If the insulation state of the dc system changes drastically, it may cause the malfunction or malfunction of the relay protection device, which will cause a great negative impact on the safe and stable operation of the power grid, and therefore it is necessary to detect the insulation resistance of the dc system.
In recent years, the following schemes mainly exist for detecting the insulation resistance of a direct current system: "design of on-line insulation monitoring device based on STM 32" published in 2019 of "computer measurement and control" in journal, a method of using an unbalanced bridge and a balanced bridge to measure insulation resistance and locate a fault point is applied, but the method has large error and is complicated to operate. "direct current system fault detection research based on improved unbalanced bridges", published in 2018 by journal of electrical automation, improves a balanced bridge method, so that faults are effectively and accurately detected, but fault points cannot be accurately positioned, and effective detection cannot be performed under the condition of strong interference of a transformer substation and the like. "220 kV substation dc ground fault analysis and finding measures" published in 2015 by "automation application" and "dc system insulation monitoring technology based on dynamic difference method" published in 2015 by "electrical engineering journal", on the basis of leakage current detection method, insulation resistance is detected by using dynamic difference method, which needs to detect leakage current to obtain leakage current variation, but this value is small and is not easy to measure accurately.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: how to provide a method for predicting the insulation fault of a direct current system, which can predict the insulation resistance at the next moment according to the historical data of the insulation resistance, thereby realizing the prepositioning of a system fault point and being beneficial to improving the positioning precision and efficiency.
In order to solve the technical problems, the invention adopts the following technical scheme:
a direct current system insulation fault prediction method based on Kalman filtering is characterized by comprising the following steps:
s1, obtaining the insulation resistance value of the direct current bus to the ground, and establishing a database of the insulation resistance of the bus to the ground;
s2, after the insulation resistance value of the direct current bus to the ground is obtained each time, the average insulation resistance of the current database is calculated
Figure BDA0002678777940000014
Figure BDA0002678777940000011
S3, calculating the average insulation resistance at the previous moment
Figure BDA0002678777940000012
Average insulation resistance from the next moment
Figure BDA0002678777940000013
Resistance difference value DeltaR ofi,i∈[1,N)；
S4, calculating the resistance difference value delta RiAnd corresponding detection interval time DeltaTiIs taken as a ratio coefficient Xi；
S5, analyzing the variation trend of the ratio coefficient by using Kalman filtering, and predicting the ratio coefficient X at the next moment;
s6, calculating the insulation resistance increase value delta R at the next moment:
ΔR＝X×T
s7, calculating the predicted insulation resistance value R at the next moment:
R＝Rn+ΔR
in the formula: rnT is the time interval between the predicted insulation resistance value R and the previous time.
Further, in step S1, the branch insulation resistance detecting unit with the following structure obtains an insulation resistance value of the dc bus to ground, and includes an instrument amplifier, detection resistors R3 and R4 serially connected between the positive electrode and the negative electrode of the dc bus, and a ground resistor R5, where one end of the ground resistor R5 is grounded, and the other end is connected between the detection resistors R3 and R4; the positive power supply end of the instrument amplifier is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power supply end of the instrument amplifier is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the instrument amplifier is grounded, and the inverting input end of the instrument amplifier is connected between the detection resistors R3 and R4; the instrument amplifier is also provided with an external gain control resistor R6.
Preferably, the instrument amplifier is an AD620 instrument amplifier.
Preferably, the first isolation power supply and the second isolation power supply are both flyback power supplies.
Furthermore, the output end of the instrument amplifier is connected with a single chip microcomputer, and a display screen is arranged on the single chip microcomputer.
Furthermore, a wireless communication module is further arranged on the single chip microcomputer.
Preferably, the wireless communication module is a bluetooth module.
Furthermore, a storage module for storing the resistance value of the detection resistor is further arranged on the single chip microcomputer.
Further, the state equation and the observation equation predicted by the kalman filter analysis are as follows:
Xk+1＝A*Xk+B*Uk+1+Wk+1
Zk+1＝H*Xk+1+Vk+1
in the formula: xkThe insulation resistance increase rate detected for the kth time; xk+1Is a predicted insulation resistance increase rate of a subsequent test; u shapek+1Is a control input; zk+1Is a state matrix Xk+1The observed quantity of (1); wk+1Is the system noise; vk+1To observe noise; A. b, H is a parameter matrix;
wherein the system noise and the observation noise satisfy the following equation:
Figure BDA0002678777940000021
E[Wk(Wk)T]＝Q,E[Vk(Vk)T]＝Y
in the formula: wkIs the system noise; vkTo observe noise; q is a covariance matrix of system noise; y is a covariance matrix of observation noise;
the state prediction equation of the Kalman filtering prediction model is as follows:
Figure BDA0002678777940000022
Figure BDA0002678777940000031
the state update equation of the Kalman filtering prediction model is as follows:
Figure BDA0002678777940000032
Figure BDA0002678777940000033
Figure BDA0002678777940000034
in the formula:
Figure BDA0002678777940000035
is known as Zk+1A previous state prediction value;
Figure BDA0002678777940000036
is known as Zk+1The optimal estimated value is obtained; k is a Kalman gain matrix and is a Kalman gain matrix,
Figure BDA0002678777940000037
representing the covariance between the true and predicted values; pk+1Representing a covariance between the true value and the optimal estimated value; and satisfies the following formula:
Figure BDA0002678777940000038
in the formula:
Figure BDA0002678777940000039
representing a prior state error; e.g. of the typek+1Indicating a posterior state error.
In summary, the invention can predict the insulation resistance at the next moment according to the historical data of the insulation resistance, thereby realizing the prepositioning of the system fault point, and being beneficial to improving the positioning accuracy and efficiency.
Drawings
Fig. 1 is a system block diagram of a branch insulation resistance detection unit.
Fig. 2 and 3 are equivalent circuit diagrams of the bridge of fig. 1.
FIG. 4 is a flow chart of the system predictive analysis in an embodiment.
Fig. 5 is a simulation diagram of insulation resistance measurement.
Fig. 6 is a graph of output voltages with positive and negative insulation resistances being the same.
Fig. 7 is a graph of output voltage when the positive insulation resistance is reduced and the negative insulation resistance is unchanged.
Fig. 8 is a graph of output voltage when the negative insulation resistance is reduced and the positive insulation resistance is unchanged.
Fig. 9 is a graph showing the variation trend of the insulation resistance value with time.
Fig. 10 is an actual view.
Detailed Description
In the embodiment, a dynamic differential response method is adopted to detect the insulation resistance of the direct current system on line, the insulation resistance value of the direct current system is calculated and updated in real time by picking up micro-voltage signals in a detection circuit, a resistance value change curve is drawn, a change trend function of the resistance value is fitted, the generation time of an alarm threshold value of the insulation resistance is estimated and predicted by using the change trend function, and the function is corrected by combining with the leakage current detection of a periodic feeder line system. According to the method, the amplifier for the high common mode input voltage instrument is used as the analog front end to monitor the insulation resistance, so that the isolation of the bus and the measuring unit and the pickup of weak signals generated by insulation resistance micro-deformation in a high-temperature and electromagnetic interference environment are realized. And the change trend of the insulation fault occurrence time and the insulation resistance value is predicted based on Kalman filtering analysis, so that the defects that a feeder insulation resistance measuring circuit in the conventional direct current feeder system is low in measurement precision, slow in response, incapable of online real-time monitoring and the like are overcome. Finally, by adopting MATLAB simulation analysis and field physical simulation test, the correctness and the feasibility of the method are verified.
The dynamic differential response online detection of the insulation resistance of the direct current system in the embodiment specifically comprises two parts, wherein the first part adopts an instrument amplifier as an analog front-end detection unit, and the insulation resistance of the positive end and the negative end of a load branch circuit to the ground is measured by adopting an AD620 with high gain, high common-mode rejection ratio and high common-rail and amplifying dynamic differential mode signals, so that the defect that the insulation resistance needs to be switched in and out in the traditional method for measuring the insulation resistance is eliminated, and the real-time monitoring of the insulation resistance is realized. And the second part is insulation fault prediction, the Kalman filtering is utilized to model insulation resistance data measured by differential dynamic response, the change trend of the insulation resistance data is predicted, a change curve is drawn, manual detection is carried out on the branch leakage current before the predicted time node of the alarm threshold value is reached, and the influence of the insulation resistance change on the measurement at the same moment is reduced.
A first part:
as shown in fig. 1, the branch insulation resistance detection unit includes an instrument amplifier, detection resistors R3 and R4 arranged in series between the positive electrode and the negative electrode of the dc bus, and a ground resistor R5, one end of the ground resistor R5 is grounded, and the other end is connected between the detection resistors R3 and R4; the positive power supply end of the instrument amplifier is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power supply end of the instrument amplifier is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the instrument amplifier is grounded, and the inverting input end of the instrument amplifier is connected between the detection resistors R3 and R4; the instrument amplifier is also provided with an external gain control resistor R6. In this embodiment, the instrument amplifier is an AD620 instrument amplifier; the first isolation power supply and the second isolation power supply are both flyback power supplies; the output end of the instrument amplifier is connected with a single chip microcomputer, and a display screen, a Bluetooth communication module and a storage module used for storing the resistance value of the detection resistor are arranged on the single chip microcomputer.
The detection unit simultaneously measures the insulation resistance of two direct current buses in a branch circuit, wherein R represents a branch circuit load, and R represents1And R2Respectively representing positive and negative DC bus line to ground insulation resistance, U is the voltage between the positive and negative buses, R3、R4To detect the resistance. The detection device is isolated from the bus by a flyback power supply to obtain electricity, and an amplifier AD620 for the instrument is used for amplifying a resistor R5And calculating the insulation resistance of the load branch circuit to the ground by the voltage at two ends.
In a natural environment, the insulation resistance of the load branch to the ground is irreversibly reduced gradually, and even under the same environment, the insulation resistance of the positive bus and the negative bus to the ground does not change in the same way in a very short time.
When the direct current system normally works, the detection device (namely the detection unit) is hung on the load branch circuit, and the ground resistance of the load branch circuit and the negative bus voltage U of the negative feedback end pair of the instrument amplifier are measured1. Adjusting the measuring resistance R by means of the balanced bridge principle3、R4Make R5The voltage at the two ends is zero, and the whole measuring device is dynamically balanced.
When the insulation resistance of the positive bus to the ground is reduced, the voltage of the positive feedback terminal of the instrument amplifier AD620 to the voltage of the negative bus is changed into U2The input voltage of the instrument amplifier is delta U, and the instrument amplifier outputs U after being amplified by G times through AD6200According to Thevenin's theorem, the bridge circuit shown in FIG. 1 can be equivalent to the two-port network shown in FIG. 2 to short-circuit the power supplyTo the circuit of FIG. 3, where UoAs an equivalent power supply, RiIs an equivalent resistance.
Therefore, the equivalent internal resistance of the bridge can be obtained:
Figure BDA0002678777940000051
according to the circuit in fig. 2, the output voltage is obtained when the load is electrically bridged:
Figure BDA0002678777940000052
Figure BDA0002678777940000053
Figure BDA0002678777940000054
Figure BDA0002678777940000055
the instrument amplifier outputs a voltage U0R is obtained after calculation1' the alarm time can be transmitted to the upper computer through the Bluetooth communication module and stored in the SD card of the upper computer so as to correct the alarm threshold time in real time.
U when insulation resistance of negative bus to ground is reduced2May be equal to U1I.e. Δ U is zero, the insulation resistance of the negative bus to ground is considered to be reduced at this time, simplifying the process of calculating the negative bus to ground. Compared with the existing method for detecting the insulation resistance, the scheme does not need to repeatedly switch the resistance, can measure the insulation resistance value of the direct current system in real time, and eliminates the measurement error of the traditional balance bridge when the insulation resistance of the positive and negative buses to the ground changes the same in a period of time. Meanwhile, the historical insulation resistance value is stored in the SD card, and an equation and a curve of the change of the insulation resistance along with time are fitted to the data of the SD card and combined with the equation and the curveAnd the equation is corrected by detecting the leakage current of the regular artificial feeder line, so that the time for the insulation resistance to reach the threshold value can be accurately predicted.
A second part:
kalman filtering prediction insulation fault analysis
In order to predict the time of an alarm threshold value so as to manually detect and correct the branch in advance, the concepts of state variables and state spaces are introduced on the basis of the Kalman filtering theory, an observation equation and a state equation are established, in the measurement completely containing noise, the state variables are updated by the estimation value of the previous moment and the observation value at the moment, and the state variables of a filter are optimally estimated by adopting a recursion algorithm according to the linear unbiased minimum mean square error estimation criterion, so that the optimal estimation of useful signals for filtering the noise is obtained.
The method for predicting the variation trend of the insulation resistance value by using the Kalman filtering algorithm comprises the following steps:
1) acquiring insulation resistance values of a direct current bus to the ground, establishing a database of the insulation resistance values of the bus to the ground, wherein the insulation resistance values are acquired by a system in the database for N times, and M data are acquired each time;
2) calculating the average value of the insulation resistance acquired each time
Figure BDA0002678777940000056
Figure BDA0002678777940000057
In the formula: rjAcquiring the actual value of the insulation resistance corresponding to the data set each time, wherein i and j are positive integers;
3) calculating the average value of the current insulation resistance
Figure BDA0002678777940000058
Average value of insulation resistance of next time
Figure BDA0002678777940000059
Difference value Δ R ofiI is equal to [1, N) to obtain the resistanceSequence of differences Δ R1、ΔR2……ΔRN；
4) Calculating the difference Δ RiAnd corresponding detection interval time DeltaTiTo obtain a ratio coefficient XiAnd forming a ratio coefficient sequence: xi、X2…… XN；
5) Analyzing the variation trend of the ratio coefficient by using Kalman filtering, and predicting the ratio coefficient X at the next moment;
6) calculating the insulation resistance increase value delta R at the next moment:
ΔR＝X×T (7)
7) and calculating to obtain the predicted insulation resistance value R at the next moment:
R＝Rn+ΔR (8)
in the formula: rnT is the time interval between the predicted insulation resistance value R and the previous time.
And establishing a branch insulation detection model according to an insulation resistance value information base detected by the dynamic differential response, processing data detected by the dynamic differential response through Kalman filtering, and correcting errors caused during measurement. The method comprises the steps of collecting micro-voltage signals in a detection circuit, calculating and updating the insulation resistance value of a direct current system in real time on the basis of established model parameters, drawing a resistance value change curve, fitting a change trend function of the resistance value, estimating and predicting the generation time of an insulation resistance alarm threshold value, and correcting the function by combining with periodic feeder line system leakage current detection. When the predicted data generate abnormal data in a certain time period, the dynamic differential response detection frequency is accelerated, the current data are corrected, if the insulation coefficient is abnormal after the error correction is finished, a fault signal is immediately sent to the processor, the fault branch is cut off, the pre-positioning of a system fault point and the estimation of the influence degree of the system fault point are realized, and the direct-current system grounding line is more efficiently and accurately positioned. The system predictive analysis flow is shown in fig. 4.
The precondition of kalman filtering is that the system noise and the measurement noise are both subjected to zero mean gaussian distribution, and the noise components are independent from each other, so the noise covariance matrix is a diagonal matrix, and therefore the following assumptions are made for the system noise and the observation noise:
Figure BDA0002678777940000061
E[Wk(Wk)T]＝Q,E[Vk(Vk)T]＝Y (10)
in the formula: wkIs the system noise; vkTo observe noise; q is a covariance matrix of system noise; and Y is a covariance matrix of observation noise. By processing the observation signal containing noise, an estimate of the true signal at the time of the minimum error can be obtained in an average sense.
If the insulation resistance increase rate of a plurality of consecutive times is taken as a group of sequences to predict the insulation resistance increase rate of the next time, the following state equation and observation equation can be established:
Xk+1＝A*Xk+B*Uk+1+Wk+1 (11)
Zk+1＝H*Xk+1+Vk+1 (12)
in the formula: xkThe insulation resistance increase rate detected for the kth time; xk+1The predicted insulation resistance increment rate of the next detection; u shapek+1Control input (typically zero); zk+1Is a state matrix Xk+1The observed quantity of (1); A. b, H is a parameter matrix.
The state prediction equation and the state updating equation in the Kalman filtering prediction model can be used as follows:
Figure BDA0002678777940000062
Figure BDA0002678777940000063
in the formula:
Figure BDA0002678777940000071
is known as Zk+1A previous state prediction value;
Figure BDA0002678777940000072
is known as Zk+1The optimal estimated value is obtained; and K is a Kalman gain matrix, and the Kalman gain matrix K is a coefficient when covariance partial derivative between the optimal estimation value and the real value is equal to 0 and is used for fusing the measurement value and the optimal estimation value.
Order:
Figure BDA0002678777940000073
wherein:
Figure BDA0002678777940000074
representing a prior state error; e.g. of the typek+1Representing a posterior state error;
Figure BDA0002678777940000075
representing the covariance between the true and predicted values; pk+1Representing a covariance between the true value and the optimal estimated value; the P matrix is used to measure the accuracy of the optimal estimate.
The following equations (11) to (15) can be obtained:
Figure BDA0002678777940000076
Figure BDA0002678777940000077
calculating a Kalman gain matrix K under the optimal estimation condition:
Figure BDA0002678777940000078
the above equations (13) and (16) are state prediction equations, and the equations (14), (17), and (18) are state update equations. Equations (13) to (18) constitute a kalman filter recursion for predicting an optimum estimated value of the insulation resistance increase rate at each time.
The model provided by the embodiment introduces external comprehensive influence factors influencing the insulation condition, selects system insulation related information, predicts the insulation resistance value of the system by using a Kalman filtering method after expanding the sample data volume, further obtains the insulation level trend, fully considers the dynamic adaptability of external factors influencing the insulation condition change of the direct current system, provides accurate prediction data, improves the insulation detection precision, and simultaneously realizes the more efficient direct current system grounding line positioning on the basis of judging the insulation level change trend of the direct current system.
Simulation and experimental result analysis
The simulation graph model as shown in FIG. 5 was created using MATLAB/Simulink software.
The change of the insulation resistance in the direct current system is simulated by artificially changing the positive insulation resistance and the negative insulation resistance, and corresponding voltage values are detected and recorded to obtain the instantaneous relations between the insulation resistance and the voltage. The voltage between the positive bus and the negative bus in the direct current system is set to be 220V. When R is1、R2、R3、R 410 M.OMEGA.R5Is 1K omega, and the external gain resistor R of the instrument amplifier6499 omega, output voltage U0Is 0 as shown in fig. 6.
R1Gradually changing from 10M omega to 2M omega, namely simulating positive insulation resistance reduction, R2、R3、R4Unchanged, R5Is 1K omega, and the external gain resistor R of the instrument amplifier6At 499 Ω, the output voltage trend graph is shown in FIG. 7.
R1Is 2M omega, R2Gradually changing from 10M omega to 2M omega, namely simulating negative insulation resistance drop, R3、R 410 M.OMEGA.R5Is 1K omega, and the external gain resistor R of the instrument amplifier6At 499 Ω, the output voltage trend graph is shown in FIG. 8.
The micro-voltage signal in the detection circuit is picked up, and after processing, a time-varying trend graph of the insulation resistance shown in fig. 9 is drawn, and the time-varying trend graph is transversely compared with data detected by a traditional detection method, and the data detected by the method is consistent with the data detected by the traditional method.
The real object device is actually measured and compared with a traditional measuring method in a certain place substation, when the insulation level is normal, the result measured by the traditional measuring method is shown in figure 10 (the time in the figure causes display errors due to the fact that debugging equipment does not correct the system time), the voltage of a section of positive bus is 253.2V, the insulation to the ground is abnormal, the insulation resistance to the ground is 100 omega, the insulation resistance measured by the device is 100 omega, and the feasibility and the accuracy of the device are verified.
The method is formed by optimization on the basis of a direct current leakage current detection method, overcomes the defect of low measurement sensitivity caused by small leakage current value, predicts the change trend of the insulation resistance by using a Kalman filtering algorithm, can judge the insulation fault in real time, accurately estimates the time for the insulation resistance to reach the alarm threshold value, and solves the defects of low measurement precision, slow response, incapability of on-line real-time monitoring and the like of a feeder insulation resistance measurement circuit in the conventional direct current feeder system.
The above description is only exemplary of the present invention and should not be taken as limiting, and any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A direct current system insulation fault prediction method based on Kalman filtering is characterized by comprising the following steps:
s1, obtaining the insulation resistance value of the direct current bus to the ground, and establishing a database of the insulation resistance of the bus to the ground;
s2, after the insulation resistance value of the direct current bus to the ground is obtained each time, the average insulation resistance of the current database is calculated
Figure FDA0002678777930000013
Figure FDA0002678777930000011
S3, calculating the average insulation resistance at the previous moment
Figure FDA0002678777930000012
Average insulation resistance from the next moment
Figure FDA0002678777930000014
Resistance difference value DeltaR ofi,i∈[1,N)；
S4, calculating the resistance difference value delta RiAnd corresponding detection interval time DeltaTiIs taken as a ratio coefficient Xi；
S5, analyzing the variation trend of the ratio coefficient by using Kalman filtering, and predicting the ratio coefficient X at the next moment;
s6, calculating the insulation resistance increase value delta R at the next moment:
ΔR＝X×T
s7, calculating the predicted insulation resistance value R at the next moment:
R＝Rn+ΔR
in the formula: rnT is the time interval between the predicted insulation resistance value R and the previous time.
2. The kalman filter-based dc system insulation fault prediction method of claim 1, wherein in the step S1, the branch insulation resistance detection unit configured as follows is used to obtain the insulation resistance value of the dc bus to ground, and includes an instrumentation amplifier, detection resistors R3 and R4 serially connected between the positive pole and the negative pole of the dc bus, and a ground resistor R5, wherein one end of the ground resistor R5 is grounded, and the other end is connected between the detection resistors R3 and R4; the positive power supply end of the instrument amplifier is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power supply end of the instrument amplifier is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the instrument amplifier is grounded, and the inverting input end of the instrument amplifier is connected between the detection resistors R3 and R4; the instrument amplifier is also provided with an external gain control resistor R6.
3. The Kalman filtering based insulation fault prediction method for the direct current system according to claim 2, wherein the instrumentation amplifier is an AD620 instrumentation amplifier.
4. The Kalman filtering based DC system insulation fault prediction method of claim 2, wherein the first isolated power supply and the second isolated power supply are flyback power supplies.
5. The Kalman filtering-based direct current system insulation fault prediction method according to claim 2, characterized in that a single chip microcomputer is connected to an output end of the instrument amplifier, and a display screen is arranged on the single chip microcomputer.
6. The Kalman filtering based direct current system insulation fault prediction method according to claim 5, characterized in that a wireless communication module is further arranged on the single chip microcomputer.
7. The Kalman filtering based DC system insulation fault prediction method of claim 6, wherein the wireless communication module is a Bluetooth module.
8. The Kalman filtering-based direct current system insulation fault prediction method according to claim 5, wherein a storage module used for storing and detecting resistance values is further arranged on the single chip microcomputer.
9. The kalman filter based dc system insulation fault prediction method according to claim 1, wherein the state equations and observation equations of the kalman filter analysis prediction are as follows:
Xk+1＝A*Xk+B*Uk+1+Wk+1
Zk+1＝H*Xk+1+Vk+1
in the formula: xkThe insulation resistance increase rate detected for the kth time; xk+1Is a predicted insulation resistance increase rate of a subsequent test; u shapek+1Is a control input; zk+1Is a state matrix Xk+1The observed quantity of (1); wk+1Is the system noise; vk+1To observe noise; A. b, H is a parameter matrix;
wherein the system noise and the observation noise satisfy the following equation:
Figure FDA0002678777930000021
E[Wk(Wk)T]＝Q,E[Vk(Vk)T]＝Y
in the formula: wkIs the system noise; vkTo observe noise; q is a covariance matrix of system noise; y is a covariance matrix of observation noise;
the state prediction equation of the Kalman filtering prediction model is as follows:
Figure FDA0002678777930000022
Figure FDA00026787779300000210
the state update equation of the Kalman filtering prediction model is as follows:
Figure FDA0002678777930000023
Figure FDA00026787779300000211
Figure FDA0002678777930000024
in the formula:
Figure FDA0002678777930000025
is known as Zk+1A previous state prediction value;
Figure FDA0002678777930000026
is known as Zk+1The optimal estimated value is obtained; k is a Kalman gain matrix and is a Kalman gain matrix,
Figure FDA0002678777930000027
representing the covariance between the true and predicted values; pk+1Representing a covariance between the true value and the optimal estimated value; and satisfies the following formula:
Figure FDA0002678777930000028
in the formula:
Figure FDA0002678777930000029
representing a prior state error; e.g. of the typek+1Indicating a posterior state error.
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