CN110889613A - Power grid system splitting risk analysis method based on SCADA big data - Google Patents
Power grid system splitting risk analysis method based on SCADA big data Download PDFInfo
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Abstract
The invention discloses a power grid system splitting risk analysis method based on SCADA big data, which comprises the following steps: s1, collecting basic data of a transmission and distribution network; s2, constructing a transmission and distribution network integrated global power grid model according to network basic data of the transmission and distribution network; s3, obtaining a final mark value of each vertex in the global power grid model; and S4, judging the disconnection risk of the power transmission and distribution network system according to the final mark value of the vertex. According to the power grid system disconnection risk analysis method based on SCADA big data, the disconnection risk assessment accuracy can be improved, weak links of a power grid can be accurately captured, and the reliability of the power grid system can be improved.
Description
Technical Field
The invention relates to the field of power grids, in particular to a power grid system splitting risk analysis method based on SCADA big data.
Background
On the basis of the traditional power grid, the smart power grid realizes further fine regulation and control of energy and power by using advanced information technology means. The efficient acquisition and processing of data are important bases for realizing fine regulation and control. With the continuous promotion of intelligent power grid construction, the generation of massive power grid data puts higher requirements on the expandability, the reliability and the expandability of the SCADA system, and the modern cloud SCADA system is urgently needed to be built, so that a foundation is laid for the later-stage excavation of the potential and the value of the large data of the power grid.
Meanwhile, in the development process of the smart grid, more and higher uncertain factors permeate into the power system, such as wide development of demand response, high-proportion renewable energy, space-time access of an energy storage system and the like. These uncertainty factors together constitute a source of risk for the power system in terms of economy, safety planning and operation. Therefore, comprehensive risk assessment and a sound risk assessment model of the power transmission and distribution network need to be developed, so that the adverse effect of uncertain factors on the power grid is effectively reduced.
At present, a risk assessment method for a transmission and distribution network can be divided into two types of certainty and probability, wherein the certainty risk assessment method mainly refers to N-K verification, namely the safe operation of the power grid under an expected accident is guaranteed, the method is used for many years in a power grid planning and dispatching department and is mature, but the method is often high in time and space complexity and cannot be suitable for connectivity tests of large-scale networks. The probabilistic risk assessment method considers the probability of element failure, can quantitatively assess the severity of element failure, but is still in the stage of starting.
Therefore, in order to solve the above problems, a power grid system splitting risk analysis method based on SCADA big data is needed, which can improve splitting risk assessment accuracy, accurately capture power grid weak links, and improve reliability of a power grid system.
Disclosure of Invention
In view of the above, the present invention aims to overcome the defects in the prior art, and provide a grid system splitting risk analysis method based on SCADA big data, which can more accurately evaluate the network splitting risks existing in the transmission network and the distribution network, thereby reducing the detection blind area of the traditional risks and effectively improving the stability of the operation of the grid system.
The invention discloses a power grid system splitting risk analysis method based on SCADA big data, which comprises the following steps:
s1, collecting basic data of a transmission and distribution network based on SCADA big data;
s2, constructing a transmission and distribution network integrated global power grid model G (v, e) according to transmission and distribution network basic data; v is a vertex set taking nodes in the transmission and distribution network as elements, and e is an edge set taking transmission lines and transformers in the transmission and distribution network as elements;
s3, setting a mark value for the vertex in the vertex set of the global power grid model G, continuously adjusting the mark value of each vertex in the vertex set to enable the mark values of the connected vertices to be equal, and obtaining a final mark value of each vertex when the mark value of each vertex in the vertex set does not change any more;
s4, judging whether the final mark values of all vertexes in the vertex set are equal or not, if so, completely communicating the transmission and distribution network, and avoiding splitting risks; otherwise, the transmission and distribution network is not completely communicated, and the splitting risk exists.
Further, in step S2, the transmission network model and the distribution network model are spliced together by boundary nodes to obtain a transmission and distribution network integrated global power grid model.
Further, in step S3, the final label value of each vertex in the vertex set v is obtained according to the following steps:
s31, acquiring the number n of vertexes in a vertex set v and the number m of edges in an edge set e of the global power grid model G;
s32, setting the mark value of a vertex i in the vertex set v as reach (i), and initializing the mark value of each vertex to enable the mark value reach (i) of the vertex i to be i; wherein i is an identifier of a vertex, 0< i < ═ n, and i is an integer;
s33, setting loop flag variables flag and k, wherein the initial value of the flag is true, and the initial value of k is 1; wherein 0< k < ═ m, k is an integer;
s34. if reach (x (k)! Reach (y (k)), then flag ═ false, reach (x (k)) ═ max { reach (x (k))), reach (y (k)) } and reach (y (k)) } reach (x (k)); if reach (x (k)) is equal to reach (y (k)), no treatment is performed; wherein x (k) and y (k) are the identifiers of the two vertices connected by edge k;
s35, making the loop flag variable k equal to k +1, and returning to execute step S34;
s36, repeating the step S35, and when k is larger than m, entering the step S37;
s37, if the loop flag variable flag is equal to true and the flag values corresponding to the vertexes in the vertex set v of the global power grid model are not updated, taking the flag value corresponding to the vertex in the vertex set v as the final flag value of the vertex; otherwise, steps S33-S36 are repeatedly performed.
Further, in step S4, when there is a risk of splitting in the power transmission and distribution network, traversing all vertices in the global power grid model, finding a vertex with the highest voltage level, and performing troubleshooting on the found vertex.
The invention has the beneficial effects that: according to the power grid system splitting risk analysis method based on the SCADA big data, the power grid network basic data are obtained through the SCADA big data, the global power grid model is built, the power grid network connectivity is checked through a combined search method, comprehensive analysis of the whole power grid network topology information of different voltage levels is achieved, network splitting risks existing in a power transmission network and a power distribution network are evaluated more accurately, accordingly, detection blind areas of traditional risks are reduced, and the running stability of a power grid system is effectively improved.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a flow chart of the algorithm of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the invention discloses a power grid system splitting risk analysis method based on SCADA big data, which comprises the following steps:
s1, collecting basic data of a transmission and distribution network based on SCADA big data;
s2, constructing a transmission and distribution network integrated global power grid model G (v, e) according to transmission and distribution network basic data; v is a vertex set taking nodes in the transmission and distribution network as elements, and e is an edge set taking transmission lines and transformers in the transmission and distribution network as elements;
s3, setting a mark value for the vertex in the vertex set of the global power grid model G, continuously adjusting the mark value of each vertex in the vertex set to enable the mark values of the connected vertices to be equal, and obtaining a final mark value of each vertex when the mark value of each vertex in the vertex set does not change any more;
s4, judging whether the final mark values of all vertexes in the vertex set are equal or not, if so, completely communicating the transmission and distribution network, and avoiding splitting risks; otherwise, the transmission and distribution network is not completely communicated, and the splitting risk exists.
In this embodiment, in step S1, the SCADA (mainly applied to data acquisition and monitoring) can display, record, store, and call information of various sensors, and the SCADA can process complex data such as video/vision, 3D models and the like besides measuring common voltage/current, liquid level/pressure/flow/temperature, frequency/count, bar code, weather, and detection instruments (vibration, sound, light, and dust), and obtain power grid network basic data from a variety of applied scenes of the power grid based on the SCADA, where the power grid network basic data mainly includes: grid architecture, basic parameters of the grid (current, voltage, resistance, power, etc.).
In this embodiment, in step S2, a power grid is modeled according to power grid network basic data; specifically, a power transmission network model and a power distribution network model are spliced through boundary nodes (root nodes of a power distribution network) to obtain a power transmission and distribution network integrated global power grid model, wherein the power transmission network model and the power distribution network model are both commonly used models in the field of power grids, the power grids are analyzed through reading in a distribution network standard cime file (a simple and efficient power grid general model description specification), the point-edge connection relation of the power grids is extracted, and the overall topology of the power grids is constructed according to the point-edge connection relation.
The global grid model may be represented by an ordered tuple set (v, e), that is, the global grid model may be represented by a graph G ═ v, e, where v is a vertex set and e is an edge set, and elements in the edge set e may also be represented by tuples (x, y), where the elements x and y belong to the vertex set v, that is, x, y ∈ v. Because the generator and the power load in the power grid system are irrelevant to the analysis method, the generator and the power load can be ignored, namely, each node in the power grid is used as the top point of the graph G, and the transmission line and the transformer in the power grid are used as the edges of the graph G.
In this embodiment, in step S3, a mark value is set for a vertex in a vertex set v of the global power grid model G, and a final mark value of each vertex in the vertex set v is obtained based on a joint search mode, which specifically includes (as shown in fig. 2):
s31, traversing the global power grid model G (v, e), and counting to obtain the number n of vertexes in a vertex set v of the global power grid model G (v, e) and the number m of edges in an edge set e;
s32, setting serial numbers for n vertexes in the global power grid model G ═ v, e according to the form of a sequence (1,2, …, i, …, n), wherein the largest serial number is n, and expressing the vertex by the serial number of the vertex; initializing all vertexes of the global power grid model, specifically, setting a reach value, wherein the reach value corresponding to the vertex with the serial number i is equal to i, namely reach (i) ═ i, 0< i < ═ n, and i is an integer; for all the vertexes, the reach value of the vertex is the serial number of the vertex;
it should be noted that the set reach value is a dependent variable corresponding to a vertex in the global power grid model, the reach value corresponding to each vertex may be continuously changed along with subsequent continuous iterative computation, for a vertex in the same connection component, the final reach value is the maximum serial number corresponding to the vertex in the connection component, where the connection component is a connected power grid network area, and the power grid network area includes a plurality of vertices and edges connecting the vertices.
S33, setting a cyclic flag variable flag and a cyclic flag variable k, wherein the flag is a Boolean variable with only two values of true and false; k is an accumulated iteration variable and can be used as a mark symbol of an edge in the global power grid model, namely 0< k < ═ m, and k is an integer; initializing flag variables flag and k, so that the flag is true and k is 1;
s34. if reach (x (k)! Reach (y (k)), flag is made false, and reach (x (k)) max { reach (x (k))), reach (y (k)) };
if reach (x (k) ═ reach (y (k)), then no operation is needed;
where x (k) and y (k) are the sequence numbers of the two vertices connected by edge k.
S35, making the loop flag variable k equal to k +1, and returning to execute step S34;
s36, repeatedly executing the step S35 until the value of the accumulated cyclic flag variable k exceeds the number m of edges in the global power grid model, namely when k is larger than m, entering the step S37;
s37, if the loop flag variable flag is equal to true and the reach values corresponding to all the vertexes in the global power grid model are not updated, taking the reach value corresponding to each vertex in the vertex set v at the moment as the final mark value of each vertex; otherwise, steps S33-S36 are repeatedly performed.
In this embodiment, in step S4, if the final mark values (that is, the final reach values) corresponding to all vertices in the global power grid model are all equal to the maximum serial number n, the power transmission and distribution network is completely connected, and there is no risk of splitting; otherwise, the transmission and distribution network is not completely communicated, and the splitting risk exists. When the power transmission and distribution network has the risk of splitting, traversing all vertexes in the global power grid model, judging the maximum voltage grade with risk in the power transmission and distribution network, namely finding the vertex with the highest voltage grade in the power grid network with the splitting condition, and performing investigation processing on the found vertex, thereby improving the reliability of the power grid system.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (4)
1. A power grid system splitting risk analysis method based on SCADA big data is characterized in that: the method comprises the following steps:
s1, collecting basic data of a transmission and distribution network based on SCADA big data;
s2, constructing a transmission and distribution network integrated global power grid model G (v, e) according to transmission and distribution network basic data; v is a vertex set taking nodes in the transmission and distribution network as elements, and e is an edge set taking transmission lines and transformers in the transmission and distribution network as elements;
s3, setting a mark value for the vertex in the vertex set of the global power grid model G, continuously adjusting the mark value of each vertex in the vertex set to enable the mark values of the connected vertices to be equal, and obtaining a final mark value of each vertex when the mark value of each vertex in the vertex set does not change any more;
s4, judging whether the final mark values of all vertexes in the vertex set are equal or not, if so, completely communicating the transmission and distribution network, and avoiding splitting risks; otherwise, the transmission and distribution network is not completely communicated, and the splitting risk exists.
2. The grid system disconnection risk analysis method based on SCADA big data as in claim 1, characterized by comprising the following steps: in step S2, the transmission network model and the distribution network model are spliced by boundary nodes to obtain a transmission and distribution network integrated global power grid model.
3. The grid system disconnection risk analysis method based on SCADA big data as in claim 1, characterized by comprising the following steps: in step S3, the final label value of each vertex in the vertex set v is obtained according to the following steps:
s31, acquiring the number n of vertexes in a vertex set v and the number m of edges in an edge set e of the global power grid model G;
s32, setting the mark value of a vertex i in the vertex set v as reach (i), and initializing the mark value of each vertex to enable the mark value reach (i) of the vertex i to be i; wherein i is an identifier of a vertex, 0< i < ═ n, and i is an integer;
s33, setting loop flag variables flag and k, wherein the initial value of the flag is true, and the initial value of k is 1; wherein 0< k < ═ m, k is an integer;
s34. if reach (x (k)! Reach (y (k)), then flag ═ false, reach (x (k)) ═ max { reach (x (k))), reach (y (k)) } and reach (y (k)) } reach (x (k)); if reach (x (k)) is equal to reach (y (k)), no treatment is performed; wherein x (k) and y (k) are the identifiers of the two vertices connected by edge k;
s35, making the loop flag variable k equal to k +1, and returning to execute step S34;
s36, repeating the step S35, and when k is larger than m, entering the step S37;
s37, if the loop flag variable flag is equal to true and the flag values corresponding to the vertexes in the vertex set v of the global power grid model are not updated, taking the flag value corresponding to the vertex in the vertex set v as the final flag value of the vertex; otherwise, steps S33-S36 are repeatedly performed.
4. The grid system disconnection risk analysis method based on SCADA big data as in claim 1, characterized by comprising the following steps: in step S4, when there is a risk of splitting in the power transmission and distribution network, traversing all vertices in the global power grid model, finding a vertex with the highest voltage level, and performing troubleshooting on the found vertex.
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447522A (en) * | 2016-06-30 | 2017-02-22 | 国网福建省电力有限公司电力科学研究院 | Full voltage sequence integrated power grid reliability and risk evaluation method |
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