CN110618313A - Online energy consumption detection and prediction device and method for train power system - Google Patents

Online energy consumption detection and prediction device and method for train power system Download PDF

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CN110618313A
CN110618313A CN201910846419.1A CN201910846419A CN110618313A CN 110618313 A CN110618313 A CN 110618313A CN 201910846419 A CN201910846419 A CN 201910846419A CN 110618313 A CN110618313 A CN 110618313A
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李明
王艳琴
王磊
王大海
张钢
李含聪
曹会阳
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Beijing Jiaotong University
CRRC Tangshan Co Ltd
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CRRC Tangshan Co Ltd
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Abstract

The invention relates to an on-line energy consumption detection and prediction device of a train power system, which comprises: the system comprises an STM32/ARM core module, a direct current voltage acquisition and conditioning module, a direct current acquisition and conditioning module, a power supply module, a CAN bus driver, an Ethernet module and an SD card; the STM32/ARM core module adopts a chip model of STM32F7671GT6, and the Ethernet module adopts a PHY chip. The invention provides an on-line energy consumption detection and prediction device and method for a train power system, which can realize the detection and prediction of the energy consumption level of the train power system; the invention has interfaces such as Ethernet, CAN bus, etc., CAN connect with other external devices, realize data transmission and communication, and realize diversification of external functions.

Description

Online energy consumption detection and prediction device and method for train power system
Technical Field
The invention relates to the field of railway trains, in particular to an online energy consumption detection and prediction device and method for a train power system.
Background
The power devices in the railway train power system converter have losses when equipment works, and the power losses can reduce the efficiency of an inverter in the system on one hand, and on the other hand, the extra energy consumption can cause the power devices to be damaged due to over-temperature, so that the normal operation of the system is influenced. Therefore, if the online detection and prediction of the energy consumption condition can be realized, corresponding decisions can be made in real time according to the energy consumption condition, and the safe operation of the system is ensured. At present, the train power system at home and abroad has no related technology which can realize energy consumption prediction, and the device disclosed by the patent can realize multiple functions of storage recording, prediction, external output, communication and the like of system energy consumption.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an on-line energy consumption detection and prediction device and method for a train power system, which can realize the detection and prediction of the energy consumption level of the train power system; the device can also be connected with other equipment, realizes the function diversification.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
an online energy consumption detection and prediction device for a train power system comprises: the system comprises an STM32/ARM core module, a direct current voltage acquisition and conditioning module, a direct current acquisition and conditioning module, a power supply module, a CAN bus driver, an Ethernet module and an SD card; the STM32/ARM core module adopts a chip model of STM32F7671GT6, and the Ethernet module adopts a PHY chip;
power supply module supplies power for other modules, and ADC of chip STM32F7671GT63The IN4 pin is connected with the DC voltage acquisition and conditioning module, and the ADC of the chip STM32F7671GT63The IN5 pin is connected with the direct current acquisition and conditioning module; the SDMMCI _ D0 pin, the SDMMCI _ D1 pin, the SDMMCI _ D2 pin, the SDMMCI _ D3 pin, the SDMMCI _ CMD pin and the SDMMCI _ CK pin of the chip STM32F7671GT6 are connected with the SD card; the LAN MDIO pin, the LANMDC pin, the LAN REF CLK pin, the LAN TXEN pin, the LAN TXD0 pin, the LAN TXD1 pin, the LAN RXD0 pin, the LAN RXD1 pin, the LAN CRS DV pin, the LAN RXEX pin and the LAN nRST pin of the chip STM32F7671GT6 are all connected with the PHY chip; the CAN1_ TX pin and the CAN1_ RX pin of the chip STM32F7671GT6 are both connected to the CAN bus driver.
The direct current voltage acquisition and conditioning module is used for realizing numerical acquisition and detection of direct current link voltage, direct current bus voltage or pantograph-catenary direct current voltage in the middle of the power system; the direct current acquisition and conditioning module is used for realizing the numerical acquisition and detection of direct current link current, direct current bus current or bow net direct current in the middle of the power system; the power supply module is used for conditioning the grade of an external power supply input into the device into the power supply grade required by the STM32/ARM core module, the direct-current voltage acquisition and conditioning module and the direct-current acquisition and conditioning module; the Ethernet module is used for realizing the data communication between the train power system online energy consumption detection and prediction device and an external computer and a train computer and is used as a function expansion data interface of the train power system online energy consumption detection and prediction device; the SD card is used for storing detected and calculated data.
The on-line energy consumption detection and prediction method for the train power system uses the on-line energy consumption detection and prediction device for the train power system, and comprises the following steps:
step 1, respectively acquiring the voltage U of an intermediate direct current link of a power system through a direct current voltage acquisition and conditioning module and a direct current acquisition and conditioning moduledcCurrent IdcCalculating the energy consumption value X (k) of the train power system at the kth sampling point through an STM32/ARM core module;
step 2, constructing an original sampling sequence X to be tested by using the energy consumption value X (k)(0)And accumulating the original sampling sequence once to obtain:
X(1)＝X(0)d1＝{X(1)(1),X(1)(2)，...，X(1)(n)} (3)
wherein d is1Is 1-AGO accumulation operator, and
X(1)whitening data which is the gray amount data X;
step 3, calculating to obtain X(0)One subtraction result sequence of (1):
X(0)d2＝{X(1)(2)d2，...，X(1)(n)d2} (5)
wherein d is2An accumulation subtraction operator;
step 4, passing X(0)Constructing an adjacent mean sequence:
X(0)(k)d3＝0.5X(1)(k)+0.5X(1)(k-1) (6)
wherein d is3Is an adjacent mean operator;
step 5, aiming at the sequence X(1)Establishing a second-order constant coefficient linear differential equation as shown in formula (7);
wherein alpha is1～α3For undetermined coefficients of differential equations, based on X by least squares(1)Fitting the discrete sampling values to obtain;
step 6, arranging the left side and the right side of the formula (7) at a sampling interval (k-1) Ts～kTsAfter internal integration, approximate to obtainTo:
formula (8) isA difference factor, processor resolvable; wherein,
coefficient β in formula (8)1～β3Obtaining the result according to a least square method;
finding beta1～β3Then substituting the formula (8) to solve X(0)(k) Then substituting the time of the future time into X by taking the time as an independent variable(0)(k) And realizing energy consumption prediction.
In step 1, calculating the power value of the train power system by a formula (1):
Pdc(tk)＝Udc(tk)*Idc(tk) (2)
in the formula (1), Pdc(tk) Represents the time tkCalculating the obtained power value of the train power system; u shapedc(tk) Represents the time tkThe voltage value of the intermediate direct current link of the power system; i isdc(tk) Represents the time tkThe current value of the intermediate direct current link of the power system is obtained.
In the step 1, calculating the energy consumption value of the train power system through a formula (2):
in the formula (2), TsFor sampling time intervals, TsThe value is taken as 1 ms; k denotes the current k-th sampling point, and x (k) denotes the energy consumption value at the k-th sampling point.
In step 6,. beta.1～β3The calculation formula of (a) is as follows:
(β1 β2 β3)＝(AT A)-1ATB (10)
wherein,
the invention has the beneficial effects that:
1. the invention can realize the on-line detection of the energy consumption of the train power system and the functions of collecting, storing, recording, displaying and the like of real-time data;
2. the invention is based on GM (2,1) model, and can realize the prediction of energy consumption level.
3. The invention has interfaces such as Ethernet, CAN bus, etc., CAN connect with other external devices, realize data transmission and communication, and realize diversification of external functions.
Drawings
The invention has the following drawings:
FIG. 1 is a schematic view of the apparatus of the present invention.
FIG. 2(a) is a schematic circuit diagram of a DC voltage acquisition and conditioning module;
fig. 2(b) is a schematic circuit diagram of the dc current collection and conditioning module.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
A train power system on-line energy consumption detection and prediction device and method are provided, which are used for acquiring the voltage U of the intermediate direct current link of a power system through a direct current voltage acquisition and conditioning module and a direct current acquisition and conditioning moduledc、IdcThe value is calculated through an STM32/ARM core module, and the energy consumption at the moment can be calculated, and real-time data storage and display are carried out; the prediction method can realize the prediction of the loss level of the power system and can predict the loss level of the power systemThe measurement result is transmitted to a manager; in addition, the device has interfaces such as Ethernet and CAN bus, and CAN transmit energy consumption data and prediction results to a computer or other external equipment in real time, thereby realizing diversification of external functions.
The schematic diagram of the on-line energy consumption detection and prediction device of the train power system is shown in fig. 1, and the device comprises: the system comprises an STM32/ARM core module, a direct current voltage acquisition and conditioning module, a direct current acquisition and conditioning module, a power supply module, a CAN bus driver, an Ethernet module and an SD card; the STM32/ARM core module adopts a chip model STM32F7671GT6, which is the core of the device, is used for data calculation processing and real-time data storage and display, and is also responsible for data communication of the display unit. The Ethernet module adopts a PHY chip.
The direct current voltage acquisition and conditioning module is used for realizing the numerical acquisition and detection of the intermediate direct current link voltage, the direct current bus voltage or the pantograph-catenary direct current voltage of the power system;
the direct current acquisition and conditioning module is used for realizing the numerical acquisition and detection of direct current link current, direct current bus current or bow net direct current in the middle of the power system;
the power supply module is used for conditioning the grade of an external power supply input into the device into the power supply grade required by the STM32/ARM core module, the direct-current voltage acquisition and conditioning module and the direct-current acquisition and conditioning module;
the Ethernet module is used for realizing data communication between the train power system online energy consumption detection and prediction device and an external computer, a train computer and the like, and is used as a function expansion data interface of the train power system online energy consumption detection and prediction device;
the SD card is used for storing the detected and calculated data;
power supply module supplies power for other modules, and ADC of chip STM32F7671GT63The IN4 pin is connected with the DC voltage acquisition and conditioning module, and the ADC of the chip STM32F7671GT63The IN5 pin is connected with the direct current acquisition and conditioning module; SDMMCI _ D0 pin, SDMMCI _ D1 pin, SDMMCI _ D2 pin, SDMMCI _ D3 pin, SDMMCI _ CMD pin, S of chip STM32F7671GT6The DMMCI _ CK pins are connected with the SD card; the LAN MDIO pin, the LANMDC pin, the LAN REF CLK pin, the LAN TXEN pin, the LAN TXD0 pin, the LAN TXD1 pin, the LAN RXD0 pin, the LAN RXD1 pin, the LAN CRS DV pin, the LAN RXEX pin and the LAN nRST pin of the chip STM32F7671GT6 are all connected with the PHY chip; the CAN1_ TX pin and the CAN1_ RX pin of the chip STM32F7671GT6 are both connected to the CAN bus driver.
Fig. 2(a) and 2(b) are schematic circuit diagrams of the dc voltage collecting and conditioning module and the dc current collecting and conditioning module, respectively. T1 is a DC voltage sensor for detecting the intermediate DC link voltage Udc(ii) a T2 is a DC sensor for detecting the current I of intermediate DC link in power systemdcThe bus bar passes through T2. +/-power supply ends of T1 and T2 are respectively connected into +15V1 DC and-15V 2 DC; m terminals of T1 and T2 are connected to a conditioning circuit at the rear stage; in the conditioning circuit of T1, when R2 is R3, U2 is 0.5 (3+ U1); in addition, the operational amplifier a1 mainly plays an isolation role and improves the equivalent impedance between U2 and a2 when U3 is equal to U2; when R4 is R6, U4 is (R5/R4) U3, and the U4 voltage corresponding terminal of the post-stage conditioning circuit of the T1 sensor is connected to the ADC of the chip STM32F7671GT63Pin IN 4. Since the conditioning circuit of the T2 is the same as the conditioning circuit of the T1, the principle of the conditioning circuit is not explained much, and therefore the UI4 voltage corresponding terminal of the conditioning circuit of the later stage of the T2 sensor is connected to the ADC of the chip STM32F7671GT63Pin IN 5.
An online energy consumption detection and prediction method for a train power system comprises the following steps:
1. the DC voltage U of the power system is respectively acquired through the DC voltage acquisition and conditioning module and the DC current acquisition and conditioning moduledcCurrent IdcCalculating the energy consumption value of the train power system through an STM32/ARM core module;
Pdc(tk)＝Udc(tk)*Idc(tk) (3)
in the formula (1), Pdc(tk) Represents the time tkCalculating the obtained energy consumption calculation value of the train power system; u shapedc(tk) Represents the time tkThe voltage value of the intermediate direct current link of the power system; i isdc(tk) Represents the time tkThe current value of the middle direct current link of the power system is measured;
in the formula (2), TsThe sampling time interval may typically be 1 mus, 1ms, or 1s or even longer if the resolution is not high. The device gives consideration to the aspects of algorithm complexity, requirements on computing hardware and the like, TsThe value is taken to be 1 ms. K in the formulas (1) and (2) represents the current k-th sampling point, and X (k) represents the energy consumption value at the k-th sampling point.
Constructing an original sampling sequence X to be measured by X (k)(0)(the sampling interval is 1 millisecond), the online energy consumption prediction method based on the gray prediction model comprises the following steps:
2. for original sampling sequence X(0)Performing 1-AGO accumulation (one-time accumulation) to obtain
X(1)＝X(0)d1＝{X(1)(1),X(1)(2)，...，X(1)(n)} (3)
Wherein d is1Is 1-AGO accumulation operator, and
by 1-AGO addition, X can be understood(1)The whitening data is gray amount data X. That is to say, the general rule of extracting the energy consumption data from the train operation data of the given interval through accumulation is studied.
3. In addition, X can also be obtained(0)A sequence of subtraction results
X(0)d2＝{X(1)(2)d2，...，X(1)(n)d2} (5)
Wherein d is2To accumulate subtraction operators.
4. By X(0)Constructing a sequence of close-proximity means
X(0)(k)d3＝0.5X(1)(k)+0.5X(1)(k-1) (6)
Wherein d is3Is the next to average operator.
5. The essence of the GM (2,1) model is for the sequence X(1)And (4) establishing a second-order constant coefficient linear differential equation as shown in the formula (7). Wherein alpha is1～α3To be determined coefficients of the differential equation, X can be based on the least square method(1)And fitting the discrete sampling values to obtain the target product.
6. The continuous time domain solution in equation (7) is difficult to obtain a corresponding numerical solution through a discrete computation process of a processor. The left side and the right side of the formula (7) are arranged at a sampling interval (k-1) Ts～kTsAfter internal integration, it is approximated
Formula (8) isThe difference factor, the processor is solvable. Wherein,
coefficient β in formula (8)1～β3Obtaining the following by a least square method:
(β1 β2 β3)＝(ATA)-1 ATB (10)
wherein
Finding beta1～β3Then substituting the formula (8) to solve X(0)(k) Substituting the time of the future time into X by using the time as an independent variable(0)(k) And the energy consumption prediction can be realized.
The prediction input sequence can be constructed by respectively adopting two input data of increment and accumulated energy consumption of energy consumption, but the obtained prediction results are very close. And thus both can be considered equivalent.
Those not described in detail in this specification are within the skill of the art.

Claims (7)

1. The utility model provides a train driving system online energy consumption detects and prediction device which characterized in that includes: the system comprises an STM32/ARM core module, a direct current voltage acquisition and conditioning module, a direct current acquisition and conditioning module, a power supply module, a CAN bus driver, an Ethernet module and an SD card; the STM32/ARM core module adopts a chip model of STM32F7671GT6, and the Ethernet module adopts a PHY chip;
power supply module supplies power for other modules, and ADC of chip STM32F7671GT63The IN4 pin is connected with the DC voltage acquisition and conditioning module, and the ADC of the chip STM32F7671GT63The IN5 pin is connected with the direct current acquisition and conditioning module; the SDMMCI _ D0 pin, the SDMMCI _ D1 pin, the SDMMCI _ D2 pin, the SDMMCI _ D3 pin, the SDMMCI _ CMD pin and the SDMMCI _ CK pin of the chip STM32F7671GT6 are connected with the SD card; the LAN MDIO pin, the LAN MDC pin, the LAN REF CLK pin, the LAN TXEN pin, the LAN TXD0 pin, the LAN TXD1 pin, the LAN RXD0 pin, the LAN RXD1 pin, the LAN CRSDV pin, the LAN RXEX pin and the LAN nRST pin of the chip STM32F7671GT6 are all connected with the PHY chip; the CAN1_ TX pin and the CAN1_ RX pin of the chip STM32F7671GT6 are both connected to the CAN bus driver.
2. The on-line energy consumption detection and prediction device of the train power system according to claim 1, characterized in that: the direct current voltage acquisition and conditioning module is used for realizing numerical acquisition and detection of direct current link voltage, direct current bus voltage or pantograph-catenary direct current voltage in the middle of the power system; the direct current acquisition and conditioning module is used for realizing the numerical acquisition and detection of direct current link current, direct current bus current or bow net direct current in the middle of the power system; the power supply module is used for conditioning the grade of an external power supply input into the device into the power supply grade required by the STM32/ARM core module, the direct-current voltage acquisition and conditioning module and the direct-current acquisition and conditioning module; the Ethernet module is used for realizing the data communication between the train power system online energy consumption detection and prediction device and an external computer and a train computer and is used as a function expansion data interface of the train power system online energy consumption detection and prediction device; the SD card is used for storing detected and calculated data.
3. An online energy consumption detection and prediction method for a train power system, which uses the online energy consumption detection and prediction device for the train power system of claim 1 or 2, and comprises the following steps:
step 1, respectively acquiring the voltage U of an intermediate direct current link of a power system through a direct current voltage acquisition and conditioning module and a direct current acquisition and conditioning moduledcCurrent IdcCalculating the energy consumption value X (k) of the train power system at the kth sampling point through an STM32/ARM core module;
step 2, constructing an original sampling sequence X to be tested by using the energy consumption value X (k)(0)And accumulating the original sampling sequence once to obtain:
X(1)＝X(0)d1＝{X(1)(1),X(1)(2)，...，X(1)(n)} (3)
wherein d is1Is 1-AGO accumulation operator, and
step 3, calculating to obtain X(0)One subtraction result sequence of (1):
X(0)d2＝{X(1)(2)d2，...，X(1)(n)d2} (5)
wherein d is2An accumulation subtraction operator;
step 4, passing X(0)Constructing an adjacent mean sequence:
X(0)(k)d3＝0.5X(1)(k)+0.5X(1)(k-1) (6)
wherein d is3Is an adjacent mean operator;
step 5, aiming at the sequence X(1)Establishing a second-order constant coefficient linear differential equation as shown in formula (7);
wherein alpha is1～α3For undetermined coefficients of differential equations, based on X by least squares(1)Fitting the discrete sampling values to obtain;
step 6, arranging the left side and the right side of the formula (7) at a sampling interval (k-1) Ts～kTsAfter the internal integration, approximation is carried out to obtain:
formula (8) isA difference factor, processor resolvable; wherein,
coefficient β in formula (8)1～β3Obtaining the result according to a least square method; t issIs a sampling time interval;
finding beta1～β3Then substituting the formula (8) to solve X(0)(k) Then substituting the time of the future time into X by taking the time as an independent variable(0)(k) And realizing energy consumption prediction.
4. The on-line energy consumption detection and prediction method of the train power system according to claim 3, characterized in that: in step 1, calculating the power value of the train power system by a formula (1):
Pdc(tk)＝Udc(tk)*Idc(tk) (1)
in the formula (1), Pdc(tk) Represents the time tkCalculating the obtained power value of the train power system; u shapedc(tk) Represents the time tkThe voltage value of the intermediate direct current link of the power system; i isdc(tk) Represents the time tkThe current value of the intermediate direct current link of the power system is obtained.
5. The on-line energy consumption detection and prediction method of the train power system according to claim 4, characterized in that: in the step 1, calculating the energy consumption value of the train power system through a formula (2):
in the formula (2), TsIs a sampling time interval; k denotes the current k-th sampling point, and x (k) denotes the energy consumption value at the k-th sampling point.
6. The on-line energy consumption detection and prediction method of the train power system according to claim 5, characterized in that: the T issThe value is taken to be 1 millisecond.
7. The on-line energy consumption detection and prediction method of the train power system according to claim 3, characterized in that: in step 6,. beta.1～β3The calculation formula of (a) is as follows:
(β1 β2 β3)＝(ATA)-1ATB (10)
wherein,
CN201910846419.1A 2019-09-09 2019-09-09 Online energy consumption detection and prediction device and method for train power system Pending CN110618313A (en)

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