JPH04127839A - Power demand estimating unit for traffic system - Google Patents

Abstract

PURPOSE:To estimate power demand of a traffic system accurately by determining power demand of each load based on data fed from an operating condition input section, with reference to the content stored in a load estimate data storing section, and summing thus determined power demand for an objective section. CONSTITUTION:An operating condition input section 1 receives operating condition data of each train and other train data as attributes. A load estimating data storing section 3 stores a table showing the correspondence between power demand and train position or estimating time for every type of train. An estimated train power calculating section 4 executes calculation of power demand for each train based on operating condition calculating conditions derived from input information fed from the operating condition input section 1 with reference to data stored in the load estimation data storing section 3. An estimated power integrating section 7 then determines the sum of estimated power demand for all objective train.

Description

[Detailed Description of the Invention] [Object of the Invention] (Industrial Application Field) The present invention provides a power consumption prediction device for a transportation system that predicts the power consumption of a transportation system powered by electricity, such as an electric railway. It is related to.

(Prior Art) Conventionally, as a power usage prediction device that predicts power usage for a certain period of time, such as 30 minutes while fishing on a boat, there is, for example, a device called a demand monitoring control device. The principle of this device is (1
) The power consumption QF' within a certain period of time is predicted 71F1.

QP=Pt-t-(ΔP/Δt)・ST...(1
) Here, Pt: Current power up to 1 minute from the start of the demand ΔAverage power at Δt before PAT minute ST: Remaining time Next, predicted excess power which is the difference from the target power Qo determined by equation (2). Qd is determined, and this QdO value is used to reflect warnings, control command outputs for load reduction, etc.

Q d = QP −Qo −
(2) The above equation (1) expresses ΔP/Δ, that is, the closer the rate of change in power usage is to a constant value, the higher the prediction accuracy of power usage QP within a certain period of time, or conversely, when the change is large, That is, if the load fluctuation is large, the error becomes large.

Therefore, although this device is currently used for objects with small load fluctuations, such as factories and buildings, it cannot be used for objects with large fluctuations, such as moving loads such as electric railway substations, where the load is concentrated at startup.

(Problems to be Solved by the Invention) As described above, there is currently no suitable device or method for predicting power consumption in a transportation system. For this reason, depending on the route, the power exceeds the contracted power in demand contracts with electric power companies, etc., and paying contract excess fees has become a big problem.

If the power consumption can be predicted appropriately, if there is a possibility of exceeding the contracted power, it can be used for trains, etc. in the target section2, or in the case of railway vehicles, it can be used in a way such as limiting the notch of the running command. It will be possible to reduce electricity consumption and avoid exceeding contracted power, which will lead to a reduction in contract excess charges.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a power usage prediction device for a transportation system that can accurately predict power usage of a transportation system in which the target load moves and fluctuates frequently.

[Structure of the Invention] (Means for Solving the Problems) In order to solve the above problems, the present invention firstly provides a method for inputting data regarding the operating status and attributes of loads such as trains that run using electricity as a power source. a load prediction data storage section that stores standard power usage prediction data for the prediction target section for each attribute of each load; Negative 6
:J Calculate the predicted power usage for each load by referring to the storage contents of the prediction side data storage unit. The gist of the present invention is to include a predicted power integration unit that calculates the sum of different predicted power usage for the prediction target section.

Second, in the configuration of Memory 1, there is a condition/data input section for inputting conditions such as load operation restrictions, and a relationship between conditions such as operation restrictions and changes in power consumption corresponding to the conditions for certain events. The adjustment knowledge base storage unit stores the rules in the form of rules that specify that a certain operation is to be performed when the condition is met, and the adjustment knowledge base storage unit stores the above information based on the storage contents of the adjustment knowledge base storage unit corresponding to the conditions input from the condition/data input unit. Plan by load? lFI
The gist of the present invention is to include a predicted power adjustment unit that adjusts the predicted power consumption determined by the electric power source.

Thirdly, in the second configuration, the load-specific predicted power calculation unit expresses the predicted power consumption for each load using a fuzzy number that takes uncertainty into account, and calculates the predicted power consumption by using the rules stored in the adjustment knowledge base storage unit. The operation section in is an operation for the fuzzy number, and the sub total 1 power integration section calculates the sum of the fuzzy numbers of predicted power consumption for each target load, and calculates the required power based on the fuzzy set of the summed predicted power consumption. The gist is that the total predicted power consumption is defuzzified by the calculation.

Fourthly, in the second or third configuration, the conditions
Input the actual power consumption and excess judgment setting value up to now in the data input section, calculate the sum of the predicted power consumption obtained from the predicted power integration section and the actual power consumption, and add this sum and the excess judgment setting value. The gist of the present invention is to include an excess determination unit that determines whether or not the excess is exceeded and the amount of the excess by comparing the values, and an output unit that outputs a required output including a warning signal in accordance with the excess of the excess determination unit.

(Function) First, standard power usage prediction data for a prediction target section is stored in the load prediction data storage unit according to attributes such as the number of vehicles such as trains that are loads and the vehicle type. The predicted power calculation unit for each load refers to the power usage data +1+11 data based on the data regarding the operation status and attributes of the load input from the operation status input unit, and calculates the child 11P for each load.
1 power consumption J is calculated, and then the predicted power integration unit calculates the total predicted power consumption for the prediction target section by summing the predicted power consumption for each member 6 (j). Accurately predict the power consumption of a transportation system with moving loads and frequent fluctuations.

Second, rules are stored in the adjustment knowledge base storage unit in the form of rules that specify that a certain operation should be performed when a certain event occurs, such as a relationship between conditions such as driving restrictions and changes in power consumption corresponding to these conditions. . The predicted power adjustment section adjusts and changes the predicted power usage calculated by the load-based predicted power calculation section based on the above-mentioned rules corresponding to the conditions such as driving restrictions inputted from the condition/data input section. As a result, the environment and conditions related to driving, such as driving restrictions and delay situations, are included in the n-calculation conditions, and power consumption can be predicted more accurately.

Thirdly, in the load interest power calculation section, the predicted power consumption for each load is expressed by a fuzzy number that takes into account uncertainty. As a result, the predicted power adjustment unit can adjust and change the predicted power consumption in accordance with conditions such as driving regulations in a flexible manner and in a manner similar to that experienced by human beings, and also handle the error range. Therefore, the T-measurement of power consumption can be performed more accurately and thoroughly.

Fourth, the excess determination section calculates the sum of the predicted power consumption and the actual power consumption, and compares this sum with the excess determination set value to determine whether or not there is an excess, and the extent of the excess. The output section then outputs the required output including a warning signal depending on the excess. This avoids exceeding the contracted power and makes it possible to reduce the payment of contract excess charges.

(Example) Embodiments of the present invention will be described below based on the drawings.

1 and 2 are diagrams showing a first embodiment of the present invention.

First, to explain the device configuration, in Fig. 1, reference numeral 1 is an operation status input unit, which inputs the current position of a load such as a train in a transportation system (hereinafter simply referred to as a "train") that runs using electricity as a power source. Train operation conditions such as train running patterns, train data as attributes such as number of vehicles and vehicle types are input.

Reference numeral 2 denotes a condition/data input section, into which conditions such as predicted time, current power usage, and operation regulations are input.

Reference numeral 3 denotes a load prediction data storage unit, which stores standard load prediction data such as a standard load pattern for a target section for each train type.

Reference numeral 4 denotes a train interest power meter hill section as a load-specific predicted power calculation section, which stores information in the load prediction data storage section 3 based on the input information of the above-mentioned operation status input section 1 and condition/data input section 2. FUZZ the predicted power for each train by referring to the contents
It has the function of finding expressions such as Y number.

Reference numeral 5 denotes an adjustment knowledge base storage unit, which stores the relationship between driving conditions that differ from normal driving patterns, such as driving restrictions, and changes in power consumption in the form of if-then rules or the like. .

6 is the predicted power adjustment section, which is a child of the standard running pattern calculated by the train meter power meter hill section 4. The II+1 electric power is changed based on the contents stored in the adjustment knowledge storage unit when there are special conditions such as operation restrictions.

7 is a predicted power integration unit, which calculates the sum of the predicted power for each train obtained by the predicted power adjustment unit 6 for all trains in the target section to obtain a predicted power consumption up to a given time. It has become.

Reference numeral 8 denotes an excess determination unit, which calculates the sum of the predicted power consumption obtained by the predicted power integration unit 7 and the actual power consumption up to that point, calculates the difference from the predetermined power provided by the contract, etc., and determines the excess. It is now possible to find the exceeded level.

Reference numeral 9 denotes a power unit which outputs a warning signal, a notch restriction command, etc. in accordance with the excess level determined by the excess determination unit 8.

The device of this embodiment is composed of the above-mentioned parts, and predicts the power consumption during a predetermined time based on conditions such as operating conditions and driving regulations, and if the child JF + power consumption exceeds a certain level, This system attempts to avoid exceeding the contracted power by outputting warning commands and notch restriction commands.

Next, the operation of the convenient power T-measuring device for a transportation system constructed as described above will be explained using FIG. 2.

In general, the mark-like load pattern when a train of a certain attribute type travels a prediction target section for a predetermined period of time is given by actual measurements or simulations, along with operating curves, etc. Load current i is expressed as a function of position X as i (x) in FIG. 2 or a typical load pattern. According to such a load pattern, when pre-pulsing the power used by train a 1, from the predicted time 1 (, to t and the position xO of train a at time to, the time and position function t (x) , the position X of train a at time t is determined.

Therefore, since x□ <x (x is the boundary of the target section), the load pattern i (x
), it can be assumed that the voltage V is almost constant, so the electric power P used in traveling from 5 to xe is given by the following equation (3).

Such a load pattern i (x) differs depending on the operating conditions such as the number of train cars, attributes such as vehicle type, and running pattern, but if this load pattern i (x) is given to each train, Based on the train's location and predicted time, the power consumption for the predicted section and time of the train can be determined.

The load prediction data storage unit 3 stores either this basic pig load pattern i (x) itself or a table showing the correspondence between train position, predicted time, and power consumption for each train type.

The train power meter unit 4 calculates the power consumption for each train based on the operation status calculation conditions based on the input information from the operation status input unit 1 by referring to the data stored in the load prediction data storage unit 3 for each train. is executed.

This calculated power usage for each train is based on the standard running pattern, so it is relatively accurate during normal times. However, it can be said that the error becomes large when there is an abnormality such as driving restrictions. The causal relationship of this change in load during abnormality is often given empirically, and in this example, the causal relationship is expressed by the following equation (4).
~ Rules are incorporated into the adjustment knowledge base storage unit 5.

If Ai then Bi ・(
4) Here, Ai: event, B1: operation. That is, when the event At is established for each train, the operation Bi is performed on the power used.

This adjustment is performed by the predicted power adjustment section 6.

Next, the predicted power integration unit 7 calculates the sum of the adjusted predicted power consumption of all target trains, and the excess determination unit 8 calculates the sum of the actual power and the total predicted power consumption, and sets the sum. A determination is made as to whether or not a certain value is exceeded. If it exceeds the limit, the output section 9 outputs a predetermined warning output.

Next, FIGS. 3 to 5 show an M2 embodiment of the present invention. This embodiment is a more specific version of the first embodiment and shows application to electric railways.

In FIG. 3, structural elements that are the same or equivalent to those in FIG. 1 are designated by the same reference numbers as above.

First, to explain the device configuration, the operation status input ■] is input from the operation management system 11 to the operation status such as the current position of the train, etc.
Train data is input as attributes such as train type, vehicle type, number of heavy vehicles, and delay time for each train.

The condition/data input section 2 is composed of a power consumption input section 21 and a condition setting section 22. The power consumption input section 21 inputs the power consumption of each substation at each point in time from the power management system 12. Reference numeral 22 is for inputting environmental condition data such as predicted time, excess judgment set values, driving regulations, and abnormal events.

The load prediction data storage unit 3 stores standard load patterns for prediction target sections for each target train type, vehicle type, and number of vehicle formations.

41 is the FUZZY number storage unit, and the child, III power is F
Since it is expressed as a UZZY number, FUZZ corresponding to each numerical value
It stores Y number of membership functions.

In addition, the train meter power meter hill section 42 is connected to the operation status input section 1.
Based on the location of each train, train type, vehicle type, and number of vehicles inputted from , and the predicted time and other information inputted from the condition setting section 22 , the contents of the load prediction data storage section 3 are referred to. The predicted power usage up to the predicted time for each train is determined, and furthermore, this determined value is expressed by the FUZZY number stored in the corresponding FUZZY number storage section 41.

The adjustment knowledge base storage unit 5 stores the delay time inputted from the operation status human horn unit 1, driving regulations inputted from the condition setting unit 22, conditions for driving different from normal driving slams such as abnormal events, and the cases thereof. The relationship between the change in the power consumption of if
-t h e n ~ rule, that is, a rule whose content is to perform a certain operation when a certain event occurs. The input conditions such as driving regulations and abnormal events are matched with the if part of if-then~lulu stored in the adjustment knowledge base storage unit 5, and the then part is adjusted according to the status of the matching. The predicted power consumption can be changed by performing the operation.

The predicted power integration unit 7 calculates the sum of the FUZZY number of predicted power for each train obtained by the predicted power adjustment unit 6 for all trains.

The excess determination unit 8 converts the entire predicted power FUZZY set obtained by the predicted power integration unit 7 into a non-FUZZY set using a centroid method or the like.
The calculated representative value is summed with the actual power consumption up to the present input from the power consumption input section 2], and the difference between the excess judgment set value set in the condition setting section 22 is calculated, and the excess and excess Make a judgment. Output #9 is configured to output a notch restriction command, an excess amount, etc. to a human such as a driving command staff in accordance with the excess amount determined by the excess determination section 8.

Next, the operation of this embodiment configured as described above will be explained in the fourth section.
This will be explained using the flowchart shown in the figure and FIG. Fifth
(A), (B), and (C) in the diagram are the predicted power usage FUZZ
(D), (E), and (F) of the same figure show the adjustment calculation results of the FUZZY output when there are conditions such as driving restrictions, and (G) of the same figure shows the predicted power consumption. The result of the summation operation of the FUZZY numbers is shown.

The operation status and train data are input from the operation status input section 1, the current power consumption is input from the power consumption input section 21, and the predicted time and various conditions are input from the condition setting section 22 (step 1).
01). Next, as explained using FIG. 2 above, the predicted power usage is calculated for each target train in the train station power meter unit 42 with reference to the storage contents of the load prediction data storage unit 3 (step 102 ). The calculated value is replaced with the corresponding FUZZY number stored in the FUZZY number storage section 41 (step 103). (A), (B), (
C) (, t, Tsuretsure #1 power consumption 40.

An example is shown in which 60 and 30 are expressed as FUZZY numbers as μaI μb1 μb2.

Next, the conditions and abnormal events inputted from the condition setting unit 22 and the operation status input unit 1 are compared with the if part of the if~then~ rule in the adjustment knowledge base storage unit 6 in the predictive power adjustment unit 6, and the An adjustment calculation is performed on the contents of the then part of the established rule (step 104). For example, as shown in Figure 5 (D), for the outbound A1 train, when the operation regulation level is A level, the rule of 25% reduction in predicted power consumption and twice the uncertainty is established, and the train The FUZZY number μa1 of the predicted power usage a1 is changed to μ81′ by the adjustment calculation in the then section.

Next, the predicted power integration unit 7 calculates the sum of the FUZZY numbers of predicted power usage for all target trains (step 105), and calculates the calculated total predicted power using the centroid method of equation (5) or the center of gravity method shown in FIG. As shown in (G), the representative value CG is determined by de-FUZZY by taking the larger of the preset membership values (Step 1
06).

CG=J', x・μc (x)dx/, I"x
tt C (x) d and set it as the predicted power consumption within a predetermined time (step 107). Then, the excess determination unit 8 compares the predicted power consumption with set values 1 and 2, which are set according to the possibility of contracted power overage. However, if the limit is exceeded, control commands such as the corresponding notch limit are outputted from the output section 9 (steps 108 to 112).

As described above, according to the second embodiment, it is possible to predict the power consumption corresponding to the operating conditions. In particular, in this embodiment, the predicted power of each train is expressed by the FUZZY number. This is because the load prediction is originally not fixed depending on the driver's driving method and has a large degree of uncertainty, so it can be said that it is closer to reality than expressing it with a scalar quantity. This allows the predicted power adjustment unit 6 to adjust the predicted power usage based on conditions in a flexible manner similar to that experienced by human beings, and can also handle error ranges.

In addition, in this second embodiment, the predicted power is adjusted for each train based on rules, which makes it possible to respond to individual train conditions such as increased load due to recovery operations due to train delays. It is possible to make detailed predictions.

Furthermore, in this second embodiment, the power consumption input unit 21 calculates the sum of the actual power consumption and the predicted power, and the excess determination unit 8 determines whether the set value is exceeded depending on the possibility of the contracted power being exceeded. However, this is a final judgment that requires the person instructing the operation of the target line section, and there is no need for any calculations or other work on the part of the person, just the output. It would be a good idea to follow.

Note that in the above embodiment, the adjustment knowledge base is i f
-the n~ rule, but in the case of simple logic, operations using conditional statements in the program instead of rule formats are sufficient. Furthermore, in the case of ambiguous logic, a FUZZY rule may be used that includes a FUZZY set in the condition part and the operation part.

[Effects of the Invention] As explained above, according to the present invention, firstly, the load prediction data storage unit stores standard information about the prediction target section for each attribute such as the number of vehicle formations and vehicle types of the trains that are the load. The predicted power consumption data for each load is stored, and the predicted power consumption by load calculation unit calculates the predicted power consumption for each load by referring to the above power consumption prediction data based on the data regarding the operation status and attributes of the load input from the operation status input unit. The predicted power integration unit calculates the total predicted power consumption for the prediction target section by calculating the sum of the sums, which allows accurate prediction of the power consumption of the transportation system where the target load is moving and fluctuates frequently. be able to.

Second, the adjustment knowledge base storage unit stores the relationship between conditions such as driving restrictions and changes in power consumption corresponding to these conditions in the form of rules that specify that a certain operation is to be performed when a certain event occurs, and the relationship is predicted. The power adjustment section makes adjustments to the predicted power usage calculated by the load-specific predicted power calculation section based on the above rules that correspond to the conditions such as operation restrictions input from the condition/data input section, so the operation Conditions such as regulations are also included in the calculation conditions, making it possible to predict power usage more accurately.

Thirdly, the predicted power consumption by load calculation section expresses the predicted power usage for each load by a fuzzy number that takes into account uncertainty, and the operating section according to the rules stored in the adjustment knowledge base storage section is calculated for the fuzzy number. Then, the predicted power integration unit takes the sum of the fuzzy numbers of predicted power usage for each target load,
Since the total predicted power consumption is defuzzified by necessary calculations based on the Fijii set of the total predicted power consumption, the predicted power consumption adjustment unit adjusts the predicted power consumption according to conditions such as operation regulations. Changes can be made flexibly and in a manner similar to that of human experiencers, and error ranges can also be included, making it possible to predict power usage more precisely and precisely.

Fourth, the excess judgment section calculates the sum of the predicted power consumption and the actual power consumption, and compares the sum with the excess judgment set value to determine whether or not the excess is exceeded, and the output section outputs the excess amount. Since the required output including a warning signal corresponding to the situation is performed, it is possible to avoid exceeding the contract power and reduce the payment of contract excess charges.

[Brief explanation of the drawing]

1 and 2 show a first embodiment of the power usage prediction device for a transportation system according to the present invention, FIG. 1 is a block diagram showing the configuration, and FIG. 2 is a calculation of the estimated train power usage. 3 to 5 show a second embodiment of the present invention, FIG. 3 is a block diagram showing the configuration, and FIG. 4 is a flow chart to explain the operation. FIG. 5 is a diagram showing an example of fuzzy number expression of predicted power consumption. 1: Operation status input section, 2: Condition/data input section, 3. Load prediction data storage section, 4: Train meter power meter hill section (load-specific predicted power π (nose section)
, 5: Adjustment knowledge base storage unit, 6: Predicted power adjustment unit, 7: Predicted power integration unit, 8: Excess determination unit, 9: Output unit, : Power consumption input unit 22: Condition setting unit, ] ZzY number storage unit .

Claims (4)

[Claims] (1) An operation status input unit that inputs data regarding the operation status and attributes of loads that run using electricity as a power source, such as trains, and standard power usage prediction data for the prediction target section for each attribute of each load. a load prediction data storage section to store, and a load-specific predicted power calculation section that calculates predicted power consumption for each load by referring to the stored contents of the load prediction data storage section based on the data input from the operation status input section. and a predicted power integration unit that sums the predicted power consumption for each load calculated by the load-specific predicted power calculation unit for the prediction target section. (2) A condition/data input section for inputting conditions such as load operation restrictions, and the relationship between conditions such as operation restrictions and changes in power consumption corresponding to the conditions, and a function that allows certain operations to be performed when certain events occur. The predicted power calculated by the load is calculated based on the adjustment knowledge base storage section that stores the contents in the form of rules, and the storage contents of the adjustment knowledge base storage section that correspond to the conditions input from the condition/data input section. 2. The power consumption prediction device for a transportation system according to claim 1, further comprising a predicted power adjustment section that adjusts the predicted power consumption. (3) The predicted power calculation unit for each load expresses the predicted power usage for each load using a fuzzy number that takes uncertainty into account, and the operation unit for the rules stored in the adjustment knowledge base storage unit expresses the predicted power consumption for each load using a fuzzy number. The predicted power integration unit calculates the sum of fuzzy numbers of predicted power consumption for each target load, and calculates the summation prediction that is defuzzified by necessary calculations based on the fuzzy set of the summed predicted power consumption. 3. The power usage prediction device for a transportation system according to claim 2, wherein the power usage prediction device is a power usage prediction device. (4) Input the actual power consumption and excess judgment setting value to date in the condition/data input section, calculate the sum of the predicted power consumption obtained from the predicted power integration section and the actual power consumption, and and the excess judgment set value to determine whether or not the excess is exceeded and the excess, and an output section that outputs a required output including a warning signal according to the excess of the excess judgment section. The power usage prediction device for a transportation system according to claim 2 or 3, characterized in that: