CN112083299B - DC system insulation fault prediction method based on Kalman filtering - Google Patents
DC system insulation fault prediction method based on Kalman filtering Download PDFInfo
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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
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- G—PHYSICS
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- 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
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Abstract
A DC system insulation fault prediction method based on Kalman filtering includes the steps of firstly, obtaining insulation resistance value of a DC bus to ground, and establishing a database of insulation resistance of the bus to ground; calculating the average insulation resistance of the current database
Average insulation resistance
Average insulation resistance from the next moment
Resistance difference DeltaR of (2) i I.e.1, N); calculating the resistance difference DeltaR i With corresponding detection interval time DeltaT i Ratio X of (2) i The method comprises the steps of carrying out a first treatment on the surface of the Analyzing the variation trend of the ratio coefficient by using Kalman filtering, and predicting the ratio coefficient X at the next moment; calculating an insulation resistance increase value delta r=x×t at the next moment; calculating to obtain a predicted insulation resistance value R=R at the next moment n +Δr; wherein: r is R n In order to predict the actual insulation resistance value of the insulation resistance value R at the previous time, T is the interval time between the predicted insulation resistance value R and the previous time. According to the invention, the insulation resistance at the next moment can be predicted according to the historical data of the insulation resistance, so that the system fault point can be pre-positioned, and the positioning precision and efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of power system detection, in particular to a DC system insulation fault prediction method based on Kalman filtering.
Background
The DC system is an important component in the power system, is widely applied to power plants, substations and other places, has huge branch networks and is used for supplying DC loads such as relay protection, control, signals, computer monitoring, accident lighting, AC uninterrupted power supply and the like. If the insulation state of the direct current system is changed drastically, the relay protection device may be refused or malfunction, which will cause great negative effect on the safe and stable operation of the power grid, so that the insulation resistance of the direct current system needs to be detected.
In recent years, the following schemes are mainly adopted for detecting the insulation resistance of a direct current system: the journal "design of an online insulation monitoring device based on STM 32" published in 2019 by computer measurement and control "measures insulation resistance by using an unbalanced bridge and a balanced bridge method, and locates fault points, but the method has larger error and complex operation. The journal electric automation is published in 2018 on the "direct current system fault detection research based on an improved unbalanced bridge", and the balanced bridge method is improved, so that faults are effectively and accurately detected, but fault points cannot be accurately positioned, and the faults cannot be effectively detected under the condition of strong interference of a transformer substation and the like. The method is characterized in that the journal is applied to the analysis and the searching measures of the direct current ground fault of the 220kV transformer substation, which are published in 2015, and the direct current system insulation monitoring technology based on the dynamic difference method, which is published in 2015, is published in the journal, and the insulation resistance is detected by the dynamic difference method on the basis of the leakage current detection method, so that the leakage current change quantity is required to be detected, but the value is smaller and is not easy to accurately measure.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to solve the technical problems that: how to provide a DC system insulation fault prediction method which can predict the insulation resistance at the next moment according to the historical data of the insulation resistance, thereby realizing the pre-positioning of the 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:
the DC system insulation fault prediction method based on Kalman filtering is characterized by comprising the following steps of:
s1, acquiring an insulation resistance value of a 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, calculating the average insulation resistance of the current database
S3, calculating the previous timeAverage insulation resistance of the etch
Mean insulation resistance from the next moment +.>
Resistance difference DeltaR of (2) i ,i∈[1,N)；
S4, calculating a resistance difference delta R i With corresponding detection interval time DeltaT i As a ratio coefficient X i ；
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 an insulation resistance increase value delta R at the next moment:
ΔR＝X×T
s7, calculating to obtain a predicted insulation resistance value R at the next moment:
R＝R n +ΔR
wherein: r is R n In order to predict the actual insulation resistance value of the insulation resistance value R at the previous time, T is the interval time between the predicted insulation resistance value R and the previous time.
Further, in the step S1, a branch insulation resistance detection unit with the following structure is adopted to obtain an insulation resistance value of the dc bus to the ground, and the branch insulation resistance detection unit comprises an amplifier for an instrument, detection resistors R3 and R4 which are arranged in series between the positive pole and the negative pole of the dc bus, and a grounding resistor R5, wherein one end of the grounding resistor R5 is grounded, and the other end is connected between the detection resistors R3 and R4; the positive power end of the amplifier for the instrument is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power end of the amplifier for the instrument is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the amplifier for the instrument is grounded, and the inverting input end of the amplifier for the instrument is connected between the detection resistors R3 and R4; the amplifier is also provided with an external gain control resistor R6.
Preferably, the amplifier for instrument is an amplifier for AD620 instrument.
As an optimization, the first isolation power supply and the second isolation power supply are flyback power supplies.
Further, the output end of the amplifier for the instrument is connected with a single-chip microcomputer, and a display screen is arranged on the single-chip microcomputer.
Further, the singlechip is also provided with a wireless communication module.
As optimization, the wireless communication module is a bluetooth module.
Furthermore, a storage module for storing the resistance value of the detection resistor is also arranged on the singlechip.
Further, the state equation and the observation equation predicted by the Kalman filtering analysis are as follows:
X k+1 ＝A*X k +B*U k+1 +W k+1
Z k+1 ＝H*X k+1 +V k+1
wherein: x is X k Insulation resistance increase rate for the kth test; x is X k+1 Insulation resistance increase rate for the predicted last detection; u (U) k+1 Is a control input; z is Z k+1 Is a state matrix X k+1 Is a observed quantity of (a); w (W) k+1 Is system noise; v (V) k+1 Is observation noise; A. b, H is a parameter matrix;
wherein, the system noise and the observed noise satisfy the following formula:
E[W k (W k ) T ]＝Q,E[V k (V k ) T ]＝Y
wherein: w (W) k Is system noise; v (V) k Is observation noise; q is a covariance matrix of system noise; y is a covariance matrix of observed noise;
the state prediction equation of the Kalman filtering prediction model is:
the state update equation of the Kalman filtering prediction model is:
wherein:
is known as Z k+1 A previous state prediction value; />
Is known as Z k+1 The optimal estimated value; k is Kalman gain matrix, ++>
Representing the covariance between the true value and the predicted value; p (P) k+1 Representing the covariance between the true value and the optimal estimate; and satisfies the following formula:
wherein:
representing a priori state errors; e, e k+1 Representing posterior state errors.
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 pre-positioning of the fault point of the system and being beneficial to improving the positioning precision 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 a system predictive analysis in an embodiment.
Fig. 5 is a simulation diagram of insulation resistance measurement.
Fig. 6 is a graph of output voltage when the positive and negative insulation resistances are the same.
Fig. 7 is a graph of output voltage with decreasing positive insulation resistance and constant negative insulation resistance.
Fig. 8 is a graph of output voltage with decreasing negative insulation resistance and constant positive insulation resistance.
Fig. 9 is a graph showing the change of the insulation resistance value with time.
Fig. 10 is a diagram of actual measurement.
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 change curve is drawn, a change trend function is fitted, the change trend function is utilized to estimate and predict the insulation resistance alarm threshold generation time, and the function is corrected by combining with the periodic leakage current detection of the feeder system. The method utilizes the amplifier for the high common mode input voltage meter as an analog front end to monitor the insulation resistance, and realizes the isolation of the bus and the measuring unit and the weak signal pickup generated by the insulation resistance micro-variation under the high temperature and electromagnetic interference environment. And the variation 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 existing direct current feeder system is low in measurement accuracy, 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 feasibility of the method are verified.
The on-line detection of the insulation resistance dynamic differential response of the direct current system in the embodiment specifically comprises two parts, wherein the first part adopts an amplifier for an instrument as an analog front end detection unit, and the defects that the insulation resistance needs to be switched in the traditional method is eliminated by adopting an AD620 isolation direct current system with high gain, high common mode rejection ratio and high common rail and amplifying dynamic differential mode signals to measure the insulation resistance of the positive end and the negative end of a load branch circuit to the ground, so that the real-time monitoring of the insulation resistance is realized. The second part is insulation fault prediction, modeling is carried out on insulation resistance data measured by differential dynamic response by using Kalman filtering, the change trend of the insulation resistance data is predicted, a change curve is drawn, and then the curve is manually detected and corrected for the leakage current of the branch before the predicted time node of the alarm threshold reaches, so that the influence of insulation resistance change at the same moment on measurement is reduced.
A first part:
as shown in fig. 1, the branch insulation resistance detection unit comprises an amplifier for an instrument, detection resistors R3 and R4 which are arranged in series between the positive electrode and the negative electrode of a direct current bus, and a grounding resistor R5, wherein one end of the grounding resistor R5 is grounded, and the other end of the grounding resistor R5 is connected between the detection resistors R3 and R4; the positive power end of the amplifier for the instrument is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power end of the amplifier for the instrument is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the amplifier for the instrument is grounded, and the inverting input end of the amplifier for the instrument is connected between the detection resistors R3 and R4; the amplifier is also provided with an external gain control resistor R6. In this embodiment, the amplifier for instrument is an amplifier for AD620 instrument; the first isolation power supply and the second isolation power supply are flyback power supplies; the output end of the amplifier for the instrument is connected with a singlechip, and the singlechip is provided with a display screen, a Bluetooth communication module and a storage module for storing the resistance value of the detection resistor.
The detection unit simultaneously measures insulation resistance of two direct current buses in the branch, wherein R represents load of the branch, and R 1 And R is 2 Respectively representing the insulation resistance of the positive and negative DC buses to the ground, U is the voltage between the positive and negative buses, R 3 、R 4 To detect resistance. The detection device is isolated from the bus through a flyback power supply to obtain electricity, and the resistor R is amplified through the amplifier AD620 for the instrument 5 The voltage across the load branch is calculated as the insulation resistance to ground.
In the natural environment, the insulation resistance to ground of the load branch circuit is irreversibly reduced gradually, and even in the same environment, the insulation resistance to ground of the positive and negative buses does not change in the same time, and in the embodiment, the insulation resistance is detected by using the output voltage of the balance bridge in an unbalanced state.
When the direct current system works normally, a detection device (i.e. a detection unit) is hung on the load branch, and the ground resistance of the load branch and the negative bus voltage U of the negative feedback end of the amplifier for the instrument are measured 1 . Adjusting the measuring resistance R by means of the balanced bridge principle 3 、R 4 Let R be 5 The voltage at 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 end of the AD620 amplifier for the instrument is changed into U 2 The input voltage of the amplifier for the instrument is delta U, and the output U is obtained after the AD620 amplification for G times 0 According to the Thevenin theorem, the bridge shown in FIG. 1 can be equivalently used as the two-port network shown in FIG. 2, and the power supply is shorted to obtain the circuit of FIG. 3, wherein U o Is equivalent to power supply, R i Is equivalent resistance.
The equivalent internal resistance of the bridge can be obtained according to the method:
according to the circuit in fig. 2, the output voltage is obtained when the load is electrically bridged:
the amplifier for the instrument outputs the voltage U 0 R is obtained after calculation 1 ' the alarm threshold 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 be corrected in real time.
U when the insulation resistance to ground of the negative bus decreases 2 May be equal to U 1 I.e., deltau is zero, the negative bus is considered to have reduced insulation resistance to ground, simplifying the process of calculating the negative bus to ground. Compared with the existing method for detecting the insulation resistance, the method has the advantages that the resistance is not repeatedly switched, the insulation resistance value of the direct current system can be measured in real time, and the measurement error of the same change of the insulation resistance of the positive bus and the negative bus to the ground in a time period of the traditional balance bridge is eliminated. And simultaneously, the historical insulation resistance value is stored in the SD card, a change equation and a curve of the insulation resistance along with time are fitted to the data, and the equation is corrected by combining the mode of detecting the leakage current of the artificial feeder line in the verification period, so that the time of the insulation resistance reaching the threshold value can be accurately predicted.
A second part:
kalman filtering predictive insulation fault analysis
In order to predict the time of the alarm threshold value, so as to carry out manual detection and correction in advance for the branch, the concepts of state variables and state space are introduced on the basis of Kalman filtering theory, an observation equation and a state equation are established, in the measurement of completely containing noise, the state variables are updated by using the estimated value of the previous moment and the observed value at the moment, and the state variables of a filter are optimally estimated by adopting a recursive algorithm according to a linear unbiased minimum mean square error estimation criterion, so that the optimal estimation of useful signals for filtering 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 an insulation resistance value of a direct current bus to the ground, and establishing a database of the insulation resistance of the bus to the ground, wherein a system in the database acquires the insulation resistance value N times, and M data are acquired each time;
2) Calculating the average value of the insulation resistance collected each time
Wherein: r is R j For each time of collecting the corresponding insulation resistance actual value in the data set, i and j are positive integers;
3) Calculating the average value of the current insulation resistance
Mean value of insulation resistance from next time->
Is a difference DeltaR of (1) i I epsilon [1, N) to obtain a resistance difference sequence DeltaR 1 、ΔR 2 ……ΔR N ；
4) Calculating the difference DeltaR i With corresponding detection interval time DeltaT i To obtain the ratio coefficient X i And (3) a ratio coefficient sequence: x is X i 、X 2 …… X N ；
5) Analyzing the variation trend of the ratio coefficient by using Kalman filtering, and predicting the ratio coefficient X at the next moment;
6) The insulation resistance increase value DeltaR at the next moment is calculated:
ΔR＝X×T (7)
7) Calculating to obtain a predicted insulation resistance value R at the next moment:
R＝R n +ΔR (8)
wherein: r is R n In order to predict the actual insulation resistance value of the insulation resistance value R at the previous time, T is the interval time 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 micro-voltage signal in the detection circuit is picked up, the insulation resistance value of the direct current system is calculated and updated in real time on the basis of the built model parameters, a resistance change curve is drawn, a change trend function of the resistance change curve is fitted, finally the generation time of the insulation resistance warning threshold is estimated and predicted, and the function is corrected by combining with the detection of the leakage current of the periodic feeder line system. When the predicted data generates abnormal data in a certain time period, the dynamic differential response detection frequency is quickened, the current data is corrected, if the insulation coefficient is abnormal after the correction is finished, a fault signal is immediately sent to the processor, a fault branch is broken, the system fault point is pre-positioned and the influence degree is estimated, and the direct current system grounding line is positioned more efficiently and accurately. The system predictive analysis flow is shown in fig. 4.
The precondition of the Kalman filtering is that the system noise and the measurement noise are subjected to zero-mean Gaussian distribution, and all noise components are mutually independent, so that the noise covariance matrixes are diagonal matrixes, and the following assumptions are made for the system noise and the observation noise:
E[W k (W k ) T ]＝Q,E[V k (V k ) T ]＝Y (10)
wherein: w (W) k Is system noise; v (V) k Is observation noise; q is a covariance matrix of system noise; y is the covariance matrix of the observed noise. By processing the observed signal containing noise, the estimated value of the true signal with the smallest error can be obtained in the average sense.
If the insulation resistance increase rate is predicted for the next time by taking the insulation resistance increase rate for several consecutive times as a set of sequences, the following state equation and observation equation can be established:
X k+1 ＝A*X k +B*U k+1 +W k+1 (11)
Z k+1 ＝H*X k+1 +V k+1 (12)
wherein: x is X k Insulation resistance increase rate for the kth test; x is X k+1 Insulation resistance increase rate for the predicted next detection; u (U) k+1 Is the control input (typically zero); z is Z k+1 Is a state matrix X k+1 Is a observed quantity of (a); A. b, H is a parameter matrix.
The state prediction equation and the state update equation in the Kalman filtering prediction model can be obtained by:
wherein:
is known as Z k+1 A previous state prediction value; />
Is known as Z k+1 The optimal estimated value; k is a Kalman gain matrix, and the Kalman gain matrix K is a coefficient when the covariance deviation between the optimal estimated value and the true value is equal to 0 and is used for fusing the measured value and the optimal estimated value.
And (3) making:
From formulas (11) to (15):
calculating a Kalman gain matrix K under the optimal estimation condition:
the above formula (13) (16) is a state prediction equation, and the formula (14) (17) (18) is a state update equation. Equations (13) to (18) constitute a kalman filter recurrence for predicting an optimal 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 enlarging the sample data volume, further obtains an insulation level trend, fully considers the dynamic adaptability of external factors influencing the change of the insulation condition of the direct current system, provides accurate prediction data, improves the accuracy of insulation detection, and simultaneously realizes more efficient positioning of the grounding circuit of the direct current system on the basis of judging the insulation level change trend of the direct current system.
Simulation and experimental result analysis
The simulation diagram model shown in fig. 5 is built by 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, so that each instantaneous relation between the insulation resistance and the voltage is obtained. The voltage between the positive bus and the negative bus in the direct current system is set to be 220V. When R is 1 、R 2 、R 3 、R 4 10MΩ, R 5 1KΩ, external gain resistor R of amplifier for instrument 6 At 499Ω, the output voltage U 0 0 as shown in fig. 6.
R 1 Gradually change from 10MΩ to 2MΩ, i.e. simulate a positive insulation resistance drop, R 2 、R 3 、R 4 Unchanged, R 5 1KΩ, external gain resistor R of amplifier for instrument 6 At 499Ω, the output voltage trend is shown in fig. 7.
R 1 Is 2MΩ, R 2 Gradually change from 10MΩ to 2MΩ, i.e. simulate a negative insulation resistance drop, R 3 、R 4 10MΩ, R 5 1KΩ, external gain resistor R of amplifier for instrument 6 At 499Ω, the output voltage trend is shown in fig. 8.
By picking up the micro-voltage signals in the detection circuit, a trend chart of the insulation resistance along with time is drawn after processing, and the trend chart is transversely compared with the data detected by the traditional existing detection method, and the method is consistent with the data detected by the traditional method.
The real object device is actually measured and compared with the traditional measuring method at a certain power substation, when the insulation level is normal, the result measured by the traditional measuring method is shown in a graph 10 (the display error is caused by the fact that the system time is not corrected by debugging equipment in the graph), the voltage of a positive bus is 253.2V, the insulation to the ground is abnormal, the insulation resistance to the ground is 100 omega, and the feasibility and the accuracy are verified by adopting the device to measure the insulation resistance to the 100 omega.
The method is formed by optimizing 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 insulation resistance by using a Kalman filtering algorithm, can judge insulation faults in real time, accurately estimates the time of the insulation resistance reaching an alarm threshold, 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 existing direct current feeder system.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. The DC system insulation fault prediction method based on Kalman filtering is characterized by comprising the following steps of:
s1, acquiring an insulation resistance value of a 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, calculating the average insulation resistance of the current database
S3, calculating the average insulation resistance at the previous moment
Mean insulation resistance from the next moment +.>
Resistance difference DeltaR of (2) i ,i∈[1,N)；
S4, calculating a resistance difference delta R i With corresponding detection interval time DeltaT i As a ratio coefficient X i ；
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 an insulation resistance increase value delta R at the next moment:
ΔR＝X×T
s7, calculating to obtain a predicted insulation resistance value R at the next moment:
R＝R n +ΔR
wherein: r is R n In order to predict the actual insulation resistance value of the insulation resistance value R at the previous moment, T is the interval time between the predicted insulation resistance value R and the previous moment;
in the step S1, a branch insulation resistance detection unit with the following structure is adopted to obtain an insulation resistance value of a direct current bus to the ground, and the branch insulation resistance detection unit comprises an amplifier for an instrument, detection resistors R3 and R4 which are arranged in series between a positive electrode and a negative electrode of the direct current bus, and a grounding resistor R5, wherein one end of the grounding resistor R5 is grounded, and the other end of the grounding resistor R5 is connected between the detection resistors R3 and R4; the positive power end of the amplifier for the instrument is connected with the positive electrode of the direct current bus through a first isolation power supply, and the negative power end of the amplifier for the instrument is connected with the negative electrode of the direct current bus through a second isolation power supply; the non-inverting input end of the amplifier for the instrument is grounded, and the inverting input end of the amplifier for the instrument is connected between the detection resistors R3 and R4; the amplifier is also provided with an external gain control resistor R6.
2. The method for predicting insulation failure of a direct current system based on Kalman filtering according to claim 1, wherein the amplifier for instrument is an AD620 amplifier for instrument.
3. The method for predicting insulation faults of a direct current system based on Kalman filtering of claim 1, wherein the first isolation power supply and the second isolation power supply are flyback power supplies.
4. The Kalman filtering-based direct current system insulation fault prediction method according to claim 1, wherein the output end of the instrumentation amplifier is connected with a single chip microcomputer, and a display screen is arranged on the single chip microcomputer.
5. The Kalman filtering-based direct current system insulation fault prediction method according to claim 4, wherein the singlechip is further provided with a wireless communication module.
6. The method for predicting insulation failure of a direct current system based on Kalman filtering of claim 5, wherein the wireless communication module is a Bluetooth module.
7. The Kalman filtering-based direct current system insulation fault prediction method according to claim 4, wherein a storage module for storing the resistance value of the detection resistor is further arranged on the single chip microcomputer.
8. The method for predicting insulation faults of a direct current system based on Kalman filtering as claimed in claim 1, wherein the state equation and the observation equation of Kalman filtering analysis prediction are as follows:
X k+1 ＝A*X k +B*U k+1 +W k+1
Z k+1 ＝H*X k+1 +V k+1
wherein: x is X k Insulation resistance increase rate for the kth test; x is X k+1 Insulation resistance increase rate for the predicted last detection; u (U) k+1 Is a control input; z is Z k+1 Is a state matrix X k+1 Is a observed quantity of (a); w (W) k+1 Is system noise; v (V) k+1 Is observation noise; A. b, H is a parameter matrix;
wherein, the system noise and the observed noise satisfy the following formula:
E[W k (W k ) T ]＝Q,E[V k (V k ) T ]＝Y
wherein: w (W) k Is system noise; v (V) k Is observation noise;q is a covariance matrix of system noise; y is a covariance matrix of observed noise;
the state prediction equation of the Kalman filtering prediction model is:
the state update equation of the Kalman filtering prediction model is:
wherein:
is known as Z k+1 A previous state prediction value; />
Is known as Z k+1 The optimal estimated value; k is Kalman gain matrix, ++>
Representing the covariance between the true value and the predicted value; p (P) k+1 Representing the covariance between the true value and the optimal estimate; and satisfies the following formula:
wherein:
representing a priori state errors; e, e k+1 Representing posterior state errors. />
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