CN102063651A - Urban power grid risk evaluation system based on on-line data acquisition - Google Patents

Abstract

An urban power grid risk assessment system based on online data collection applied in the field of online operation analysis of urban power grids. The system includes a power grid operation data collection module, a power grid data conversion modeling module, a risk assessment module, a web server, and a web rich client application program. The data source of the risk assessment system; the urban power grid risk module, the web server, and the web rich client application program complete the classification, processing and release of the risk assessment results. The urban power grid risk assessment system based on online data collection, through the collection, screening, and conversion of online grid data, establishes an online risk assessment calculation model for urban power grids, analyzes the risk degree of urban power grids online, and publishes risk assessment results and risk warning information online.

Description

An urban power grid risk assessment system based on online data collection

technical field

The invention belongs to the operation analysis of urban power grid, and in particular relates to an urban power grid risk assessment system based on online data collection.

Background technique

The risk of urban power grid refers to the loss and probability of loss caused by the urban power grid due to the uncertainty of the load in the urban power grid and the uncertainty of the equipment failure mode within a certain period of time. The root of urban power grid risk lies in the probabilistic characteristics of its behavior. There are uncertainties in the influence of equipment failures, loads, external natural and human factors in the power grid, and these factors may cause local or large-scale power outages in the system. The probabilistic risk assessment of urban power grid is to identify the possibility of failure events and the severity of consequences by establishing indicators that characterize system risk, and to reflect the probability attributes of urban power grid behavior, load changes, and component failures; determine the acceptable risk level of the system and risk control measures. At present, risk assessment methods are mostly used in the field of power grid planning. Long-term statistical data is used to analyze the risk of power grids in a long period of time (years), without the need to obtain and use online data of power grids.

With the rapid development of the urban power grid, the structure of the power grid is becoming more and more complex, and there are more and more risk factors inside and outside the power grid that threaten the safe operation of the power grid, which brings great challenges to the operation and management of the power grid. At present, although the EMS system can perform power grid security analysis and calculation, the application mode of its network analysis function is mainly executed after receiving the dispatcher's request, focusing on the research mode, lacking automatic tracking of power grid operation status and online discovery of power grid security problems function. The introduction of probabilistic risk assessment in the operation and management of urban power grids can reflect the probabilistic attributes of power system behavior, load changes, and component failures, and give the quantified value of the risk level of the urban power grid, which is an important supplement to the deterministic analysis method of urban power grid operation .

Contents of the invention

The purpose of the present invention is to apply the risk assessment method to the online monitoring and early warning of the power grid operation. Through the collection, screening and conversion of the online data of the power grid, an online risk assessment calculation model of the urban power grid is established for analysis and calculation. Whether the safety standard is met within hours or days; if it is not met, it will give the comprehensive measure of the possibility and degree of deviation of the power grid within the corresponding period of time, that is, the operation risk of the power grid, and point out the risk caused by the power grid The corresponding risk factors and events, and finally display the analysis results in a three-dimensional visualization manner, so as to improve the safe operation level of the urban power grid and reduce power outage losses. It has great social and economic value to ensure the safe operation of urban power grid.

The invention proposes an urban power grid risk assessment system based on online data collection, including a power grid operation data acquisition module (1), a power grid data conversion modeling module (2), a risk assessment module (3), a web server (4) and web rich client application (5);

The power grid operation data acquisition module (1), power grid data conversion modeling module (2) and risk assessment module (3) are respectively installed on different computers connected to each other through a local area network, or installed on the same computer, and connected to each other through a local area network The database server of the power grid dispatching management system is connected; the web server (4) is connected with the computer installed with the power grid operation data acquisition module (1), the power grid data conversion modeling module (2) and the risk assessment module (3) through a local area network, The web rich client application program (5) is installed on a computer with a web browser, and is connected to the web server through a local area network. The web rich client application program (5) is developed by Adobe Flex technology and runs on the web On the browser, a web application with rich graphic display capabilities.

The present invention also proposes an evaluation method using the above-mentioned system, comprising the following steps:

(1) Grid operation data acquisition module (1) Read grid basic equipment data, grid topology data, load forecast data, operation history data and real-time operation data from the database server of the grid dispatching management system through the network;

(2) Grid data conversion modeling module (2) According to the data obtained in step 1, obtain information such as the opening and closing status of various switches in the grid, the connection relationship of lines, the connection status of transformer taps, etc., and then establish the grid network structure and a model of how it works;

(3) Risk assessment module (3) Perform state estimation calculation on the current operation of the power grid;

(4) Risk assessment module (3) Calculate the ground state power flow according to the state estimation result;

(5) risk assessment module (3) calculate the historical tide every 15 minutes of the previous day according to the state estimation result;

(6) Risk assessment module (3) Use Monte Carlo algorithm to judge the probability of failure of various equipment based on weather and other disaster risk data and historical data that may affect the operation of power grid equipment;

(7) Risk assessment module (3) Calculate the N-k state power flow according to the ground state power flow;

(8) Risk assessment module (3) Comparing the calculation results of each power flow and the probability of occurrence, and calculating various risk assessment values;

(9) The web server (4) uses the calculation results to form a risk assessment report web page;

(10) web rich client application program (5) obtain the evaluation report page from the web server and display it.

The beneficial effects of the present invention are:

(1) Established the structure, mode and system for the collection, screening, conversion and modeling of urban power grid online risk assessment calculation and analysis data;

(2) The risk assessment method is applied to the online assessment and analysis of the urban power grid, realizing the application of the risk assessment method from offline to online, from planning analysis to operation analysis;

(3) The data flow of the risk analysis results of the urban power grid is designed, and a structural system for visually displaying risks is established.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings.

Fig. 1 is a schematic diagram of the system structure according to the present invention, showing the basic structure and data flow of the system.

Fig. 2 is a schematic diagram of the grid risk assessment process according to the present invention, showing the main steps of how to obtain the risk assessment result from the grid operation data.

Detailed ways

The power grid operation data involved in the method of the present invention is quite large, and the power calculation algorithms used are complex, and most of the algorithms are common knowledge, so no detailed description is required. The following is only a simple example:

1) Taking the distribution network in a certain area as an example, first obtain static data: including network structure and network parameters, which need to be obtained from the grid dispatching automation database through the data interface; then obtain dynamic data: including network online measurement data, which also need to be obtained through data The interface is obtained from the grid dispatching automation database;

2) Perform topology analysis on the basis of these data. Topology analysis is to transform the network node/switch model (electrical model) into a model/branch model (calculation model) according to the switch state, providing a basis for risk analysis and calculation.

3) Carry out load forecasting, and the load forecasting result is an important basic parameter of risk assessment. The evaluation period of the online risk assessment of the urban power grid includes the current period and the next few days. It is necessary to establish a load forecast curve in the next few days through short-term and ultra-short-term load forecasting methods. For long-term risk assessment, a generalized load level classification table can be established through load data processing, and the corresponding distribution model can be used to characterize the uncertainty of the load.

4) Select the expected accident set, including N-1 faults and preset N-k faults. For different analysis periods and analysis objectives, different expected accident sets can be selected. Predicted accident analysis is based on the set predicted accident set, through power flow calculation, to analyze the components that exceed the limit (load limit, voltage limit) under different predicted accident states, and calculate the degree of limit violation of the component.

5) Carry out risk index calculation, and calculate the risk indicators by calculating the probability of the failure state sampled in the Monte Carlo simulation and the limit index calculated by the failure state, including the operation limit online risk index (OOPRI) , Loss of Load Online Risk Index (LOPRI), Loss of Electricity Online Risk Index (EOPRI), Low Voltage Online Risk Index (VOPRI).

Taking the OOPRI as an example, the OOPRI is used to indicate that the grid operating parameters of the urban power grid during the evaluation period, including transformer load, line power flow, bus voltage, etc., exceed the operating limit. value probability. Operators and planners can judge the possibility of the risk that the system operating state does not meet the operating constraints based on the value of the OOPRI index. Calculated as follows:

$\mathrm{<span\; class="notranslate">\; OOPRI</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Cond</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; Cond</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; OSev</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; Cond</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Where: n is the number of load patterns predicted within the evaluation period;

m is the number of predicted accidents in the predicted accident set;

Pr(Cond _i ) is the probability of the predicted load pattern;

Pr(E _j |Cond _i ) is the probability of an expected accident under a certain load mode;

OSev(E _j , Cond _i ) is the consequence evaluation parameter, where overload is 1 and no overload is 0.

The calculation process of OOPRI is:

1. Select a load mode;

2. Select a set of expected accidents;

3. For power flow calculation, the power flow optimization and correction sub-module is notified when a limit violation occurs, and OSev(E _j , Cond _i ) is 0 if the limit violation does not occur;

4. Optimizing and correcting the power flow, OSev(E _j , Cond _i ) is 1 if the limit is still exceeded after correction, otherwise it is 0;

5. Whether you have selected the expected accident set, if not, go back to 2, which is the next step;

6. Whether to choose the load mode or not, return to 1, which is the next step;

7. Use the formula to calculate OOPRI.

The invention has been described above in terms of specific exemplary embodiments. Appropriate substitutions or modifications will be apparent to those skilled in the art without departing from the scope of the present invention. The exemplary embodiments are illustrative only, and not limiting of the scope of the invention, which is defined by the appended claims.

Claims (2)

1. urban distribution network risk evaluating system based on the online acquisition data is characterized in that:

Comprise operation of power networks data acquisition module (1), electric network data conversion MBM (2), risk evaluation module (3), web server (4) and web rich client applications (5);

Described operation of power networks data acquisition module (1), electric network data conversion MBM (2) and risk evaluation module (3) are installed in respectively on the mutual various computing machine that is connected by LAN (Local Area Network), or be installed on same the computing machine, be connected by the database server of LAN (Local Area Network) with the grid dispatching management system; Web server (4) is connected with the computing machine that operation of power networks data acquisition module (1), electric network data conversion MBM (2) and risk evaluation module (3) have been installed by LAN (Local Area Network), web rich client applications (5) is installed in one to be had on the web browser computing machine, be connected with the web server by LAN (Local Area Network), described web rich client applications (5) is utilized the Flex of Adobe company technological development, operate on the web browser, possess the weblication of abundant pattern exhibition power.

2. appraisal procedure of using the described system of claim 1 is characterized in that may further comprise the steps:

(1) operation of power networks data acquisition module (1) reads electrical network basic equipment data, topological structure of electric data, load prediction data, operation history data and real-time running data by network from the database server of grid dispatching management system;

(2) electric network data conversion MBM (2) draws the folding condition of various switches in the electrical network according to the data that step 1 obtains, the annexation of circuit, and information such as the tap connection status of transformer, and then set up the model of the electrical network network structure and the method for operation;

(3) risk evaluation module (3) is carried out state estimation calculating to the current ruuning situation of electrical network;

(4) risk evaluation module (3) is calculated the ground state trend according to the state estimation result;

(5) risk evaluation module (3) according to the state estimation result calculate the previous day every 15 minutes historical trend;

(6) risk evaluation module (3) is used the Monte Carlo algorithm, judges the probability that various device breaks down according to the weather that may influence the grid equipment operation and other disaster risk data and historical data;

(7) risk evaluation module (3) is calculated N-k state trend according to the ground state trend;

(8) risk evaluation module (3) compares each time calculation of tidal current, and the probability that occurs, and calculates various risk assessment values;

(9) web server (4) utilizes result of calculation, forms the Risk Assessment Report web page;

(10) web rich client applications (5) obtains the assessment report page from the web server, and shows.

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