METHOD FOR ANALYZING CORRELATION BETWEEN RAIL TRANSIT AND DIRECT CURRENT (DC) MAGNETIC BIAS OF TRANSFORMER

Abstract

A method for analyzing a correlation between rail transit and direct current (DC) magnetic bias of a transformer includes the following steps: A: obtaining a current of a feed cable and a DC magnetic bias current: measuring the feed cable current in rail transit and the DC magnetic bias current of a transformer in a power grid within a certain period by a monitoring apparatus; B: calculating a characteristic quantity of the feed current within the measurement period based on the obtained current of the feed cable; C: calculating a characteristic quantity of the DC magnetic bias current within the measurement period based on the DC magnetic bias current; and D: calculating a support degree and a confidence coefficient based on the calculated characteristic quantity of the feed current and the calculated characteristic quantity of the DC magnetic bias current, and generating a correlation rule.

Claims

1. A method for analyzing a correlation between rail transit and direct current (DC) magnetic bias of a transformer, comprising the following steps: A: obtaining a current of a feed cable and a DC magnetic bias current: measuring the current of the feed cable in rail transit and the DC magnetic bias current of a transformer in a power grid within a certain period by a monitoring apparatus; B: calculating a characteristic quantity of the feed current within the measurement period based on the obtained current of the feed cable in step A; C: calculating a characteristic quantity of the DC magnetic bias current within the measurement period based on the DC magnetic bias current obtained in step A; and D: calculating a support degree and a confidence coefficient based on the calculated characteristic quantity of the feed current in step B and the calculated characteristic quantity of the DC magnetic bias current in step C, and generating a correlation rule.

2. The method for analyzing a correlation between rail transit and DC magnetic bias of a transformer according to claim 1, wherein in step B, the calculating a characteristic quantity of the feed current within the measurement period based on the obtained current of the feed cable in step A specifically comprises: assuming that there are a total of N subway stations, the measurement period is T, one measurement point is recorded every one second, a feed current of an i.sup.th subway station DT.sub.i at a time point t is IF.sub.i,t, and a feed current threshold is IF.sub.i,tv; and if IF.sub.i,t<IF.sub.i,tv, determining that a value of a characteristic quantity CF.sub.i,t of the feed current of the subway station DT.sub.i at the time point t is 0, wherein 1≤i≤N and 1≤t≤T; or if IF.sub.i,t≥IF.sub.i,tv, determining that a value of a characteristic quantity CF.sub.i,t of the feed current of the subway station DT.sub.i at the time point t is 1, wherein 1≤i≤N and 1≤t≤T.

3. The method for analyzing a correlation between rail transit and DC magnetic bias of a transformer according to claim 2, wherein in step C, the calculating a characteristic quantity of the DC magnetic bias current within the measurement period based on the DC magnetic bias current obtained in step A specifically comprises: assuming that there are a total of M transformer substations, the measurement period is T, one measurement point is recorded every one second, a DC magnetic bias current of a j.sup.th transformer substation BD.sub.j at the time point t is IS.sub.j,t, and a DC magnetic bias current threshold is IS.sub.j,tv; and if IS.sub.j,t<IS.sub.j,tv, determining that a value of a characteristic quantity CS.sub.j,t of the DC magnetic bias current of the transformer substation BD.sub.j at the time point t is 0, wherein 1≤j≤M and 1≤t≤T; or if IS.sub.j,t≥IS.sub.j,tv, determining that a value of a characteristic quantity CS.sub.j,t of the DC magnetic bias current of the transformer substation BD.sub.j at the time point t is 1, wherein 1≤j≤M and 1≤t≤T.

4. The method for analyzing a correlation between rail transit and DC magnetic bias of a transformer according to claim 3, wherein in step D, the calculating a support degree and a confidence coefficient based on the calculated characteristic quantity of the feed current in step B and the calculated characteristic quantity of the DC magnetic bias current in step C, and generating a correlation rule specifically comprises: generating an item set P={p.sub.1, p.sub.2, . . . , p.sub.T} within the measurement period T based on the calculated characteristic quantities of the feed current and the DC magnetic bias current, wherein each second corresponds to one item; there are a total of T items in the item set P; the item only comprises a subway station and a transformer substation whose characteristic quantities are 1; and the time point t is used as an example, and if characteristic quantities of all the N subway stations and M transformer substations at the time point are 1, an item p.sub.t corresponding to the time point t shall comprise the N subway stations and the M transformer substations; obtaining, from the item set and based on an Aprior algorithm, a frequent item set whose support degree and confidence coefficient are both greater than 80%; and generating a rule of a strong correlation between a subway station and a transformer substation based on the obtained frequent item set, and analyzing a correlation between the feed current in rail transit and the DC magnetic bias current of the transformer.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] FIG. 1 is a schematic flowchart of an embodiment of a method for analyzing a correlation between rail transit and DC magnetic bias of a transformer according to the present disclosure.

DETAILED DESCRIPTION

[0025] The technical solutions in the present disclosure are clearly and completely described below with reference to the accompanying drawings in the present disclosure.

[0026] FIG. 1 is a schematic flowchart of an embodiment of a method for analyzing a correlation between rail transit and DC magnetic bias of a transformer according to the present disclosure. The method includes the following steps:

[0027] A: Obtain a current of a feed cable and a DC magnetic bias current: measure the current of the feed cable in rail transit and the DC magnetic bias current of a transformer in a power grid within a certain period by a monitoring apparatus (for example, a clamp ammeter).

[0028] B: Calculate a characteristic quantity of the feed current within the measurement period based on the obtained current of the feed cable in step A. This step specifically includes:

[0029] assuming that there are a total of N subway stations, the measurement period is T, one measurement point is recorded every one second, a feed current of an i.sup.th subway station DT.sub.i at a time point t is IF.sub.i,t, and a feed current threshold IF.sub.i,tv; and

[0030] if IF.sub.i,t<IF.sub.i,tv, determining that a value of a characteristic quantity CF.sub.i,t of the feed current of the subway station DT.sub.i at the time point t is 0, where 1≤i≤N and 1≤t≤T; or

[0031] if IF.sub.i,t≥IF.sub.i,tv, determining that a value of a characteristic quantity CF.sub.i,t of the feed current of the subway station DT.sub.i at the time point t is 1, where 1≤i≤N and 1≤t≤T.

[0032] C: Calculate a characteristic quantity of the DC magnetic bias current within the measurement period based on the DC magnetic bias current obtained in step A. This step specifically includes:

[0033] assuming that there are a total of M transformer substations, the measurement period is one measurement point is recorded every one second, a DC magnetic bias current of a j.sup.th transformer substation BD.sub.j at the time point t is IS.sub.j,t, and a DC magnetic bias current threshold is IS.sub.j,tv; and

[0034] if IS.sub.j,t<IS.sub.j,tv, determining that a value of a characteristic quantity CS.sub.j,t of the DC magnetic bias current of the transformer substation BD.sub.j at the time point t is 0, where 1≤j≤M and 1≤t≤T; or

[0035] if IS.sub.j,t≥IS.sub.j,tv, determining that a value of a characteristic quantity CS.sub.j,t of the DC magnetic bias current of the transformer substation BD.sub.j at the time point t is 1, where 1≤j≤M and 1≤t≤T.

[0036] D: Calculate a support degree and a confidence coefficient based on the calculated characteristic quantity of the feed current in step B and the calculated characteristic quantity of the DC magnetic bias current in step C, and generate a correlation rule. This step specifically includes:

[0037] generating an item set P={p.sub.1, p.sub.2, . . . , p.sub.T} within the measurement period T based on the calculated characteristic quantities of the feed current and the DC magnetic bias current, where each second corresponds to one item; there are a total of T items in the item set P; the item only includes a subway station and a transformer substation whose characteristic quantities are 1; and the time point t is used as an example, and if characteristic quantities of all the N subway stations and M transformer substations at the time point are 1, an item p.sub.t corresponding to the time point t shall include the N subway stations and the M transformer substations;

[0038] obtaining, from the item set and based on an Aprior algorithm, a frequent item set whose support degree and confidence coefficient are both greater than 80%; and

[0039] generating a rule for a strong correlation between a subway station and a transformer substation based on the obtained frequent item set, and analyzing a correlation between the feed current in rail transit and the DC magnetic bias current of the transformer.

[0040] The technical solution in the present disclosure is described in detail below with reference to one specific embodiment.

[0041] Step A: Measure currents of feed cables of two subway stations and DC magnetic bias currents of five transformers in a survey region synchronously for 20 minutes by a clamp ammeter, where one value is recorded every one second.

[0042] Step B: Calculate characteristic quantities of the feed currents of the two subway stations (DT.sub.1 and DT.sub.2) within the measurement period.

[0043] Step C: Calculate characteristic quantities of the DC magnetic bias currents of five transformer substations (BD.sub.1, BD.sub.2, BD.sub.3, BD.sub.4, and BD.sub.5) within the measurement period.

[0044] Step D: Generate an item set P={p.sub.1, p.sub.2, . . . , p.sub.T} within the measurement period T based on the calculated characteristic quantities of the feed currents in step B and the calculated characteristic quantities of the DC magnetic bias currents in step C. A time point t is used as an example. At the time point, only characteristic quantities of the subway station DT.sub.1, the subway station DT.sub.2, the transformer substation BD.sub.1, and the transformer substation BD.sub.3 are 1. Therefore, p.sub.t={DT.sub.1, DT.sub.2, BD.sub.1, BD.sub.3}. Similarly, an item set can be obtained for another time point.

[0045] Based on an Aprior algorithm, a frequent item set F={DT.sub.1, BD.sub.1, BD.sub.3} whose support degree and confidence coefficient are both greater than 80% is obtained from the item set P. There are nonempty subsets {DT.sub.1}, {BD.sub.1}, {BD.sub.3}, {DT.sub.1,BD.sub.1}, {DT.sub.1,BD.sub.3}, and {BD.sub.1,BD.sub.3} in the frequent item set. Therefore, the following correlation rules can be generated. It can be seen that the following rules are strongly correlated.

TABLE-US-00001 Rule Confidence coefficient DT.sub.1 .fwdarw. BD.sub.1, BD.sub.3 99% BD.sub.1 .fwdarw. DT.sub.1, BD.sub.3 95% BD.sub.3 .fwdarw. DT.sub.1, BD.sub.1 96% BD.sub.1, BD.sub.3 .fwdarw. DT.sub.1 99% DT.sub.1, BD.sub.3 .fwdarw. BD.sub.1 96% DT.sub.1, BD.sub.1 .fwdarw. BD.sub.3 95%

[0046] The above described are merely specific implementations of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any modification or replacement easily conceived by those skilled in the art within the technical scope of the present disclosure shall fall within the protection scope of the present disclosure.