CN104966173A - Method and system for monitoring state of power grid - Google Patents

Abstract

The invention discloses a method for monitoring a state of a power grid, and the method comprises the steps: obtaining a state parameter of the power grid; building a local network intelligent agent according to the state parameter of the power grid; building a global network intelligent agent based on the local network intelligent agent; and carrying out the monitoring and control of the state of the power grid through the global network intelligent agent. The method achieves the monitoring and control of the state of the power grid, and solves a technical problem of intelligent control of the power grid. The invention also discloses a system for monitoring the state of the power grid.

Description

A kind of electric network state method for supervising and system

Technical field

The invention belongs to electrical network field of intelligent control technology, particularly relate to a kind of electric network state method for supervising and system.

Background technology

Although the research of various countries' intelligent grid is different with planning, be consistent to the fundamental requirement of intelligent grid, namely electrical network should " stronger, more intelligent ".Strong is requirement to smart grid security, namely when electrical network generation large disturbances and fault, still can keep the power supply capacity to user, and not occurrence of large-area power outage; Under disaster, extreme weather conditions or still can ensure the safe operation of electrical network under outside destroy; There is the ability guaranteeing security information for power system.The intellectuality of intelligent grid is embodied in: (1) Observable: adopt advanced sensing measurement technology, realize the accurate perception to electrical network; (2) can control: can control effectively to object of observation; (3) real-time analysis and decision-making: realize from data, information to the lifting of intelligent decision making.4) self-adaptation and self-healing: realize Automatic Optimal adjustment and fault self-recovery.Self-healing property refers to by continuing to monitor grid equipment running status, utilize the real-time information obtained from sensor and measurement mechanism etc. automatically to predict system problem, diagnose, decision-making and control etc., reduce because fault causes the scope and time of power failure, maximum possible ensures system power quality, safety in operation and reliability etc.Just as the immunologic function of human body, to have in real time, online and continuous print safety assessment and analysis ability, powerful early warning and prevention and control ability, and automatic fault prediction, fault diagnosis, fault isolation and system self-recovery ability.Thus, intelligent grid intellectuality realizes needing technology in electrical network observation, systematic analysis, control decision, adaptive control etc. constantly to improve and promote.

In intelligent grid Study of intelligent with in realizing, bias toward concept and theoretical frame research, practical actualizing technology, theoretical method etc. are still in exploration and experimental phase more.

Summary of the invention

In view of this, the embodiment of the present invention is expected to provide a kind of electric network state method for supervising and system, to realize the Based Intelligent Control to electrical network.

The technical scheme of the embodiment of the present invention is achieved in that

Embodiments provide a kind of electric network state method for supervising, described method comprises:

Obtain electric network state parameter;

Localized network intelligent body is built according to described electric network state parameter;

Global network intelligent body is built based on described localized network intelligent body;

By described global network intelligent body to the monitor and forecast of electric network state.

In such scheme, described acquisition electric network state parameter comprises:

Obtain the operational factor of the designated equipment of described electrical network; Described operational factor is sent to the data processing unit of the transformer station at described designated equipment place.

In such scheme, describedly build localized network intelligent body according to described electric network state parameter and comprise:

Analysis is carried out to described electric network state parameter and obtains grouped data;

The localized network intelligent body described transformer station being carried out to state estimation is configured for according to described grouped data.

In such scheme, describedly build global network intelligent body based on described localized network intelligent body and comprise:

Set up the data channel between described localized network intelligent body;

By described data channel by described localized network intelligent body composition global network intelligent body.

In such scheme, describedly to be comprised by the monitor and forecast of described global network intelligent body to electric network state:

Described global network intelligent body carries out analysis to described electric network state parameter and obtains state estimation information;

The control strategy of operation of power networks requirement is met according to described state estimation information;

Described control strategy is sent to the transformer station that described localized network intelligent body is corresponding;

Described transformer station adjusts grid equipment according to described control strategy.

The embodiment of the present invention additionally provides a kind of electric network state supervisory system, and described system comprises:

Data acquisition facility, for obtaining electric network state parameter;

Local data's construction device, for building localized network intelligent body according to described electric network state parameter;

Global data construction device, for building global network intelligent body based on described localized network intelligent body;

State monitoring device, for passing through described global network intelligent body to the monitor and forecast of electric network state.

In such scheme, described data acquisition facility comprises:

Sensor, for obtaining the operational factor of the designated equipment of described electrical network;

Wireless launcher, for being sent to the data processing unit of the transformer station at described designated equipment place by described operational factor.

In such scheme, described local data construction device comprises:

Data processing unit, obtains grouped data for carrying out analysis to described electric network state parameter;

Localized network construction unit, for being configured for the localized network intelligent body described transformer station being carried out to state estimation according to described grouped data.

In such scheme, described global data construction device comprises:

Path Setup unit, for setting up the data channel between described localized network intelligent body;

Global network construction unit, for forming global network intelligent body by described data channel by described localized network intelligent body.

In such scheme, described state monitoring device comprises:

Data analysis unit, obtains state estimation information for carrying out analysis by described global network intelligent body to described electric network state parameter;

Strategy generating unit, for being met the control strategy of operation of power networks requirement according to described state estimation information;

Information transmitting unit, for sending to described control strategy the transformer station that described localized network intelligent body is corresponding.

The electric network state method for supervising that the embodiment of the present invention provides and system, first electric network state parameter is gathered, then localized network intelligent body is built according to electric network state parameter, and then build global network intelligent body, achieve the monitor and forecast to electric network state, solve the technical matterss such as the Based Intelligent Control of electrical network.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of the electric network state method for supervising of embodiment 1;

Fig. 2 is the composition structural representation of the electric network state supervisory system of embodiment 2;

Fig. 3 is the transformer health policy model schematic of embodiment 3;

Fig. 4 is the transformer health policy three-tiered evaluation model of embodiment 3;

Fig. 5 is transformer fault rate curve figure corresponding to the Repair of Transformer mode of embodiment 3;

Fig. 6 is the transformer health control schematic flow sheet of embodiment 3.

Embodiment

In the following description, by description multiple different aspect of the present invention, but, for those skilled in the art, can only utilize some or all structure of the present invention or flow process to implement the present invention.In order to the definition explained, set forth specific number, configuration and order, but clearly, also can implement the present invention when there is no these specific detail.In other cases, in order to not obscure the present invention, will no longer be described in detail for some well-known features.

Embodiment 1

In order to contribute to the realization of electrical network intelligentized control method, present embodiments provide a kind of electric network state method for supervising, as shown in Figure 1, the present embodiment said method comprising the steps of:

Step S101: obtain electric network state parameter;

In reality, the structure of electrical network and composition are extremely complicated, very difficult to whole parameter acquisitions of electrical network.Meanwhile, the normal operation of electrical network be ensured, also not need to consider whole parameters, as long as collect the parameter affecting electrical network and normally run.Therefore, electric network state parameter herein refers to and normally runs to electrical network the parameter played a key effect.Electric network state parameter can Real-time Obtaining, also can obtain by interval setting-up time, and concrete needs determines according to actual conditions.

Step S102: build localized network intelligent body according to described electric network state parameter;

Although electrical network is very complicated, also can delimit as multiple relatively independent entirety according to network mechanism, the electrical network as transformer station extremely covered integrally is considered.In reality, described electric network state parameter in transformer station is processed, obtain localized network intelligent body, namely localized network intelligent body is that the intellectuality of a local intelligence electrical network realizes subsystem, can realize condition assessment of power grid and the function such as system regulation and management of local.

Step S103: build global network intelligent body based on described localized network intelligent body;

Be distributed with multiple transformer station in electrical network, namely electrical network comprises multiple localized network intelligent body.In electrical network, certain transformer station is not independent operating, and this transformer station needs to carry out parameters of electric power with other transformer stations alternately, jointly realizes the normal operation of electrical network.Therefore, need each localized network intelligent body to be also integrated into global network intelligent body, realize the overall control to whole electrical network.Therefore, global network intelligent body refers to and can realize data interaction between localized network intelligent body and communicate, make each localized network intelligence physical efficiency self information of place partial electric grid of comprehensive utilization and information of other subsystems, thus whole electrical network is realized to the system of monitoring management.

Step S104: by described global network intelligent body to the monitor and forecast of electric network state.

After global network intelligent body collects and affects electric network state parameter that electrical network normally runs, according to the mutual relationship between these electric network state parameters, and electrical network normally runs the condition of demand fulfillment, just can determine how the relevant device of electrical network is adjusted, the condition making electric network state parameter meet electrical network normally to run, thus achieve the Intellectualized monitoring of electrical network.

First the present embodiment method gathers electric network state parameter, then builds localized network intelligent body according to electric network state parameter, and then builds global network intelligent body, achieves the monitor and forecast to electric network state, solves the technical matterss such as the Based Intelligent Control of electrical network.

Concrete, step S101 comprises: the operational factor obtaining the designated equipment of described electrical network, as the Monitoring Data etc. of transformer; Described operational factor is sent to the data processing unit of the transformer station at described designated equipment place.

Step S102 comprises:

Step S1021: analysis is carried out to described electric network state parameter and obtains grouped data;

The electric network state parameter obtained role in electrical network is different, also different on the impact of operation of power networks, dissimilar electrical network parameter is carried out classification and can obtain, to " qualitative " grouped data of electric network influencing, being convenient to monitor electrical network on the whole.

Step S1022: be configured for the localized network intelligent body described transformer station being carried out to state estimation according to described grouped data.

Take transformer station as base unit, require described grouped data to combine according to operation of power networks, form localized network intelligent body, can assess the state of transformer station.

Step S103 comprises:

Step S1031: set up the data channel between described localized network intelligent body;

Data channel is realized by cable usually, also can be realized by wireless data communications, specifically determine according to actual conditions.

Step S1032: by described data channel by described localized network intelligent body composition global network intelligent body.

Localized network intelligent body is communicated by data channel, just can form the global network intelligent body of electrical network, realize the global monitoring to electrical network by global network intelligent body.

Step S104 comprises:

Step S1041: described global network intelligent body carries out analysis to described electric network state parameter and obtains state estimation information; State estimation information is for characterizing the running status of current electric grid.

Step S1042: the control strategy being met operation of power networks requirement according to described state estimation information; State estimation information is calculated, obtains the control strategy for electrical network particular device.

Step S1043: described control strategy is sent to the transformer station that described localized network intelligent body is corresponding;

Power equipment in the usual Dou Shi transformer station of described electrical network particular device, therefore, needs first control strategy to be sent to transformer station.

Step S1044: described transformer station adjusts grid equipment according to described control strategy.

Transformer station adjusts according to the running status of power equipment (i.e. electrical network particular device) corresponding to control strategy, realizes the monitoring to electrical network.

Embodiment 2

The present embodiment and embodiment 1 belong to same inventive concept, and present embodiments provide a kind of electric network state supervisory system, as shown in Figure 2, described in the present embodiment, system comprises:

Data acquisition facility 201, for obtaining electric network state parameter;

In reality, the structure of electrical network and composition are extremely complicated, very difficult to whole parameter acquisitions of electrical network.Meanwhile, the normal operation of electrical network be ensured, also not need to consider whole parameters, as long as collect the parameter affecting electrical network and normally run.Therefore, electric network state parameter herein refers to and normally runs to electrical network the parameter played a key effect.Electric network state parameter can Real-time Obtaining, also can obtain by interval setting-up time, and concrete needs determines according to actual conditions.

Local data's construction device 202, for building localized network intelligent body according to described electric network state parameter;

Although electrical network is very complicated, also can delimit as multiple relatively independent entirety according to network mechanism, the electrical network as transformer station extremely covered integrally is considered.In reality, described electric network state parameter in transformer station is processed, obtain localized network intelligent body, namely localized network intelligent body is that the intellectuality of a local intelligence electrical network realizes subsystem, can realize condition assessment of power grid and the function such as system regulation and management of local.

Global data construction device 203, for building global network intelligent body based on described localized network intelligent body;

Be distributed with multiple transformer station in electrical network, namely electrical network comprises multiple localized network intelligent body.In electrical network, certain transformer station is not independent operating, and this transformer station needs to carry out parameters of electric power with other transformer stations alternately, jointly realizes the normal operation of electrical network.Therefore, need each localized network intelligent body to be also integrated into global network intelligent body, realize the overall control to whole electrical network.Therefore, global network intelligent body refers to and can realize data interaction between localized network intelligent body and communicate, make each localized network intelligence physical efficiency self information of place partial electric grid of comprehensive utilization and information of other subsystems, thus whole electrical network is realized to the system of monitoring management.

State monitoring device 204, for passing through described global network intelligent body to the monitor and forecast of electric network state.

After global network intelligent body collects and affects electric network state parameter that electrical network normally runs, according to the mutual relationship between these electric network state parameters, and electrical network normally runs the condition of demand fulfillment, just can determine how the relevant device of electrical network is adjusted, the condition making electric network state parameter meet electrical network normally to run, thus achieve the Intellectualized monitoring of electrical network.

Concrete, described data acquisition facility 201 comprises sensor and wireless launcher; Wherein, sensor is for obtaining the operational factor of the designated equipment of described electrical network; Wireless launcher is used for the data processing unit of the transformer station described operational factor being sent to described designated equipment place.In addition, except being obtained except operational factor by sensor, the history data of described designated equipment or the basic parameter of described designated equipment self can also directly be called.

Described local data construction device 202 comprises data processing unit and local network struction unit, and wherein, data processing unit is used for carrying out analysis to described electric network state parameter and obtains grouped data; Localized network construction unit is used for being configured for according to described grouped data the localized network intelligent body described transformer station being carried out to state estimation.

Described global data construction device 203 comprises Path Setup unit and global network construction unit, and wherein, Path Setup unit is for setting up the data channel between described localized network intelligent body; Global network construction unit is used for described localized network intelligent body composition global network intelligent body by described data channel.

Described state monitoring device 204 comprises data analysis unit, strategy generating unit and information transmitting unit, and wherein, data analysis unit is used for carrying out analysis by described global network intelligent body to described electric network state parameter and obtains state estimation information; Strategy generating unit is used for the control strategy being met operation of power networks requirement according to described state estimation information; Information transmitting unit is used for described control strategy to send to the transformer station that described localized network intelligent body is corresponding.

Embodiment 3

The present invention is described in detail by an actual scene for the present embodiment.

The present embodiment is for transformer, and the present invention will be described, comprises the following steps:

Step one: the state parameter obtaining transformer;

State parameter comprises the detection data that the sensor by arranging on the transformer records, as various operational factor; The historical data of transformer, Static State Index and hardware parameter etc. can also be comprised.

Step 2: build localized network intelligent body according to described state parameter;

All state parameters are carried out Classifying Sum, obtains the localized network intelligent body about transformer.

Step 3: build global network intelligent body based on described localized network intelligent body;

By data channel, localized network intelligent body is built into global network intelligent body, realizes the integral monitoring to network transformer operational factor.

Step 4: by described global network intelligent body to the monitor and forecast of transformer state.

Concerning the monitor and forecast of transformer state mainly for transformer health status, transformer health status is equivalent to the operation conditions of transformer in the present invention, can be divided into the health control under normal operating health control and fault state to control (management) correspondence of the health status of transformer.Fault state is divided into again catastrophic failure and Hidden fault.The health control of catastrophic failure is conceived to fault diagnosis after fault occurs and maintenance process usually, existingly at present studies comparatively fully.The present embodiment mainly pays close attention to transformer health control under normal operation.Under normal operation, with transformer health index for Main Basis, set up risk evaluation model, propose repair based on condition of component strategy.The present embodiment to the schematic diagram of transformer health control as shown in Figure 3.As can be seen from Figure 3, according to intelligent grid operation characteristic and requirement, transformer health control strategy comprises the health status judgement of three operation conditionss and the Analysis of Countermeasure of correspondence, thus realizes the health control to transformer.The key problem of the present embodiment is under normal circumstances, the analysis of the state estimation of transformer and corresponding repair based on condition of component.Health control strategy under transformer normal operation is: first utilize Monitoring Data, historical data, Static State Index and the parameter relevant to transformer state, adopts three-tiered evaluation method to carry out health evaluating to transformer, draws its health index; Then calculate its probability of malfunction according to health index, carry out risk assessment in conjunction with breakdown loss, obtain the risk cost that transformer is current; Finally formulate repair based on condition of component strategy in conjunction with the cost of overhaul.

(1) health index

The present embodiment adopts the three-tiered evaluation method in overall approach to carry out health evaluating to operating transformer, draws health index.The three-tiered evaluation model adopted as shown in Figure 4.

First order assessment models selects manufacturer, model specification and designed life, the time limit that puts into operation, operating load, running environment index as evaluate parameter, these parameters all directly reflect transformer body health status, how long put into operation, working strength and ageing loss, be most important parameter in transformer health evaluating.The assessment result of first order assessment models is the health index between 0 ~ 1, and the computing formula of health index is:

${\mathrm{TH}}_1={\mathrm{TH}}_0\×e^{B\×(T_1-T_0)}$

Wherein, TH ₁for equipment T ₁the one-level assessment health index in time; TH ₀for equipment T ₀the health index in time (putting into operation the time); E is natural constant; T ₁for the time (being generally current year) of health index will be calculated; T ₀for the time corresponding with the time of putting into operation.

Mainly choose monitoring variable in the assessment models of the second level to analyze transformer life loss.Choose hottest spot temperature and insulated electro field strength is analyzed, establish the assessment models of heat ageing and electricity-heat ageing, and calculate heat ageing health index TH respectively ₂with electricity-heat ageing health index TH ₃.

Heat ageing health index TH ₂computing formula be:

${\mathrm{TH}}_2=\underset{i_y=1}{\overset{365N}{\Σ}}\left(L\%\right)$

Wherein, i _yfor days running; N is for running year number; L% is the thermal lifetime loss percentage in units of sky.The computing formula of L% is:

$L\%=\frac{F_{EQA}\×t}{L_N}$

Wherein, t is the monitoring time that life loss rate L% is corresponding, is taken as 1 day here (24 hours); L _nfor the life expectance that can normally run transformer insulated in ecotopia, provided by statistics; F _eQAfor the equivalent accelerated deterioration factor.

If monitoring time is spaced apart Δ t, time and temperature cycle are f then in period demand _eQAcan be expressed as:

$F_{EQA}=\frac{\underset{n=1}{\overset{N}{\Σ}}{F}_{{\mathrm{AA}}_n}\×{\mathrm{\Δt}}_n}{\underset{n=1}{\overset{N}{\Σ}}{\mathrm{\Δt}}_n}$

Wherein, be the accelerated deterioration factor of 1 year, if hottest spot temperature is θ _hST, then the F under given load factor and environment temperature _aAfor:

$F_{AA}=e^{(\frac{1500}{383}-\frac{1500}{{\mathrm{\θ}}_{HST}+273})}$

Concrete value in formula draw from document [ytterbium view. based on the power transformer life cycle management maintenance decision research of running status and life appraisal. University Of Chongqing, electrical engineering, 2014].

Electricity-heat ageing health index TH ₃computation process and heat ageing health index TH ₂similar, only need will speed up aging factor F _aAbe changed to F ' _aAbring above-mentioned calculation procedure into and just can obtain electricity-heat ageing health index TH ₃:

${\mathrm{TH}}_3=\underset{i_y=1}{\overset{365N}{\Σ}}{\left(L^{\′}\%\right)}_i$

$F_{AA}^{\′}=\frac{\underset{0}{\overset{t}{\∫}}{\left(\frac{b_0}{kt+b_0}\right)}^{-(n-bt)}\·e^{-B_{f}T}dt}{t}$

Wherein, b ₀for voltage initial value in monitoring periods; K is change in voltage slope in monitoring periods; B is correction factor; B _ffor the activation energy of heat ageing reaction; T is the difference of reference temperature and absolute temperature.

First third level assessment is set up set of factors with quantity of state in first order assessment and second level assessment, accounts for obtain proportion, utilize the method for expert estimation to carry out weighted value and determine, finally obtain comprehensive health index according to three amounts in assessment.Again according to the outward appearance of transformer, the reliability of accident number of times and sleeve pipe show that correction factor carries out the correction of health value respectively.

If the assessment result of first order assessment and second level assessment is:

TH _m＝(TH ₁,TH ₂,TH ₃)

The result of expert estimation is α, represents the weighted value of three assessment results of first order assessment and second level assessment, that is:

α＝(α ₁,α ₂,α ₃) ^T

The then comprehensive health index TH of first order assessment and second level assessment _comfor:

${\mathrm{TH}}_{com}={\mathrm{TH}}_m\×\mathrm{\α}=\left[\begin{array}{ccc}{\mathrm{TH}}_1& {\mathrm{TH}}_2& {\mathrm{TH}}_3\end{array}\right]\×\left[\begin{array}{c}{\mathrm{\α}}_1\\ {\mathrm{\α}}_2\\ {\mathrm{\α}}_3\end{array}\right]={\mathrm{TH}}_1\×{\mathrm{\α}}_1+{\mathrm{TH}}_2\×{\mathrm{\α}}_2+{\mathrm{TH}}_3\×{\mathrm{\α}}_3$

If outward appearance correction factor is F ₁, defect correction coefficient is F ₂, sleeve pipe reliability correction factor is F ₃, then the final assessment health index TH of third level assessment is:

TH＝TH _com×F ₁×F ₂×F ₃

By three-tiered evaluation, consider transformer performance and to degenerate time dependent basic law, the work operation conditions of transformer and the structural modifications of transformer, obtain the health index that is comprehensively commented transformer state.

(2) risk cost

(2.1) probability of malfunction

Along with ager process and the performance degradation of transformer, the possibility that transformer breaks down is large in change.The health index TH (TH ∈ (0,1)) of transformer is the quantizating index of a characterization device health status and reliability standard, and TH is more higher close to 0 indication equipment reliability standard, more lower close to 1 indication equipment reliability standard.TH is along with increasing gradually the service time of equipment.According to existing research, can determine that both relations are class index functions:

λ＝K×e ^C×TH

Wherein, λ is probability of equipment failure; K is scale-up factor; C is coefficient of curvature.Proportional coefficient K and coefficient of curvature C need to solve, the simplest directly method is exactly the angle from mathematical statistics, usually health index is divided into some intervals, total number of units of adding up transformer in each health index interval and the number of units of the transformer broken down, then obtain the corresponding relation between health index and probability of malfunction by curve.This method regularity of distribution derives from the statistical study to great amount of samples number, and reliability is high, but needs the accuracy of a large amount of sample data guarantee results.The scale-up factor drawn by least square method with reference to pertinent literature herein and the possibility predication of coefficient of curvature, get K=0.0118, C=0.0479, then have:

λ＝0.0118×e ^0.0479×TH

(2.2) risk assessment

The present embodiment regards the risk assessment value of transformer as a risk cost, the loss when probability namely broken down and fault, adopts following formula to determine:

Risk＝λ×L

Wherein, Risk is risk cost; L is the loss that fault causes.

Probability of malfunction is released by health index above, below will from the viewpoint of system risk, fault restoration cost, personal security risk, environmental risk four breakdown loss.In the present embodiment, the computing formula of L is:

L＝L ₁+L ₂+L ₃+L ₄

Wherein, L ₁for system risk; L ₂for fault restoration cost; L ₃for personnel's security risk; L ₄for environmental risk.

First, from the angle of venture analysis, fault in various degree causes loss in various degree, so press fault severity level, to repair the time of needs for index, transformer fault is divided into total generic failure, seriousness fault and bust three kinds, is defined as:

H ₁: total generic failure---the fault can repaired in 24 hours;

H ₂: seriousness fault---need the repair time of 2-10 days;

H ₃: bust---need the repair time of more than 10 days;

By the statistical study of pertinent literature, general probability of malfunction r ₁, seriousness probability of malfunction r ₂with catastrophic probability of malfunction r ₃be respectively 64.2%, 32.1%, 3.7%.

Breakdown loss is determined by following formula respectively:

1. system risk L ₁after referring to that transformer breaks down, the economic loss that excision load causes.

Wherein, S _nfor the capacity of transformer; for average power factor; I% is the load factor of transformer; F _iindication transformer excises the probability of load under different faults; T _ithe trouble duration of indication transformer under different faults; F _iand T _iby table 1 value; θ is unit electricity value-at-risk, weighs, get empirical value θ=10472.1 Renminbi/megawatthour with electrogenesis ratio (namely unit quantity of electricity is to the contribution degree of GDP); β ₁for system risk correction factor, β ₁=β ₁₁× β ₁₂× β ₁₃, comprise the important coefficient β of transformer station ₁₁, load important coefficient β ₁₂, maintenance environmental coefficient β ₁₃, value is as shown in table 2.

Excision load probability under table 1 transformer different faults and trouble duration

	Total generic failure	Seriousness fault	Bust
				F i	1％	5％	5％
T i	24h	120h	240h

Table 2 voltage transformer system risk correction factor

2. fault restoration cost L ₂be estimated as:

$L_2=\underset{i=1}{\overset{3}{\Σ}}{C}_i\×r_i\×{\mathrm{\β}}_2$

Wherein, C _ithe rehabilitation cost of indication transformer under different faults, according to existing research obtaining value method as table 3.

Rehabilitation cost (unit) under table 3 transformer different faults

	Total generic failure	Seriousness fault	Bust
				110kV fault restoration cost	10000	100000	1800000
220kV fault restoration cost	20000	200000	5000000
				500kV fault restoration cost	30000	280000	8000000

Wherein, β ₂for fault restoration cost correction factor, β ₂=β ₂₁× β ₂₂, comprise manufacturer's factor beta ₂₁, maintenance environmental coefficient β ₂₂, its value is as table 4.

Table 4 transformer fault rehabilitation cost correction factor

3. personal security risk L ₃refer to the loss of the security incident that fault causes.

$L_3=\underset{i=1}{\overset{3}{\Σ}}{S}_i\×r_i$

Wherein, S _ifor the equivalent personal security cost of transformer under different faults, value is as shown in table 5.

Equivalent personal security cost under table 5 transformer different faults

	Total generic failure	Seriousness fault	Bust
				Equivalence personal security cost S i	20000	250000	500000

4. environmental risk L ₄after referring to that transformer breaks down, due to oil reveal, air release and the environmental loss that reason causes such as fault is on fire.

$L_4=\underset{i=1}{\overset{3}{\Σ}}{E}_i\×r_i$

Wherein, E _ithe equivalent environment cost of indication transformer under different faults, value is as shown in table 6.

Equivalent environment cost under table 6 transformer different faults

	Total generic failure	Seriousness fault	Bust
				Equivalent environment cost	10000	100000	200000

(3) repair based on condition of component strategy

A. maintenance order

The health evaluating index TH of transformer characterizes health status and the reliability standard of equipment, has directly reacted the probability of malfunction of equipment, by health index divide status of equipment and Strategies of Maintenance Comment gathers as shown in table 7.

Table 7 presses status of equipment and the Strategies of Maintenance Comment gathers of health index division

Health index	Status of equipment	Strategies of Maintenance
			＜0.35	Well	Normal operation
0.35～0.5	Better	Strengthen monitoring
			0.5～0.6	Generally	In good time arrangement maintenance
0.6～0.8	Note	To give priority in arranging for maintenance
			＞0.8	Dangerous	Arrange maintenance at once or change

This Comment gathers derives from a large amount of power transformer practical operating experiences, and the maintenance order policies that it can be used as repair based on condition of component is rational.

B. maintenance mode

The Mode of condition-oriented overhaul of transformer is divided into continuation operation, light maintenance, overhaul and replacing four kinds.Different maintenance mode is different on the impact of transformer fault rate, as shown in Figure 5.Continue to run and do not affect failure rate; Light maintenance and overhaul can make failure rate rollback certain value, and its size can represent with equivalent rollback time limit y; Replacing makes failure rate return back to initial value.

By consulting data of literatures, obtaining the experimental formula calculating the equivalent rollback time limit, being limited to t for running year _ntransformer, the equivalent rollback time limit of overhaul can be calculated as follows:

$y_d=\left\{\begin{array}{cc}\frac{9}{15}t+1& 0\≤t_n\≤15\\ 10& 15\≤t_n\≤30\\ 34-\frac{4}{5}t& t_n\≥30\end{array}\right.$

Light maintenance equivalent rollback year be limited to y _g=1.Then after repair based on condition of component, the equivalence operation time limit of transformer can be expressed as:

Run after the time limit can calculate and take various maintenance modes by equivalence, the change of transformer risk cost.First, equivalence corresponding for each maintenance mode is run the time limit according to the funtcional relationship of health index in first order assessment with the time of putting into operation, and the correction formula in third level assessment, the health index TH of equipment after calculating maintenance _j, TH _x, TH _dand TH _grepresent the health evaluating index continued after operation, light maintenance, overhaul, replacing respectively; Then pressed the risk cost of the rear equipment of risk cost formulae discovery maintenance, new risk cost is set to Risk _j, Risk _x, Risk _dand Risk _g, represent the risk cost continued after operation, light maintenance, overhaul, replacing respectively, and set as follows:

${\mathrm{TH}}_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{TH}}_j& i=1;\\ {\mathrm{TH}}_x& i=2;\\ {\mathrm{TH}}_d& i=3;\\ {\mathrm{TH}}_g& i=4.\end{array}\right.;{\mathrm{Risk}}_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{Risk}}_j& i=1;\\ {\mathrm{Risk}}_x& i=2;\\ {\mathrm{Risk}}_d& i=3;\\ {\mathrm{Risk}}_g& i=4.\end{array}\right.$

(wherein, i=1 correspondence continues to run; The corresponding light maintenance of i=2; The corresponding overhaul of i=3; I=4 correspondence is changed).

Maintenance process all can produce certain expense, available cost of overhaul CO _ti () represents, continuation operation, light maintenance, overhaul, replacing are set to CO respectively _j, CO _x, CO _dand CO _g, that is:

${\mathrm{CO}}_t\left(i\right)=\left\{\begin{array}{cc}{\mathrm{CO}}_j& i=1;\\ {\mathrm{CO}}_x& i=2;\\ {\mathrm{CO}}_d& i=3;\\ {\mathrm{CO}}_g& i=4.\end{array}\right.$

If CO _j, CO _x, CO _dand CO _gknown, can set up mathematical model of optimization, with the cost of overhaul and risk cost sum for objective function, solve the minimum value of objective function under constraint condition, the maintenance mode that the minimum value of objective function is corresponding is best maintenance mode.This mathematical model of optimization is as follows:

min[CO _t(i)+Risk _t(i)]

$s.t.\left\{\begin{array}{c}{t}_n^{\′}\left(i\right)=t_n\×k(i,1)+(t_n-y_x)\×k(i,2)+(t_n-y_d)\×k(i,3)\\ {\mathrm{TH}}_t\left(i\right)={\mathrm{TH}}_0\×e^{B\×t_n^{\′}\left(i\right)}\\ {\mathrm{\λ}}_t\left(i\right)=0.0118\×e^{0.0479\×{\mathrm{TH}}_t\left(i\right)}\\ {\mathrm{Risk}}_t\left(i\right)={\mathrm{\λ}}_t\left(i\right)\×L\end{array}\right.,i=1,2,3,4.$

Wherein, i is maintenance mode, i=(1,2,3,4), represents respectively and continues operation, light maintenance, overhaul and replacing maintenance mode; CO _ti () is the cost of overhaul of i-th kind of maintenance mode; Risk _ti () is the risk cost of i-th kind of maintenance mode; t _nfor running the time limit; T ' _ni () is that the equivalence of i-th kind of maintenance mode runs the time limit; TH _ti () is the health index of i-th kind of maintenance mode; TH ₀for initial health index; B is aging coefficient; λ _ti () is the probability of malfunction of i-th kind of maintenance mode; L is breakdown loss; K is contrast function, $k(i,j)=\{\begin{array}{cc}1& i=j\\ 0& i\≠j\end{array}.$

According to the model of transformer health control, and the analysis of repair based on condition of component based on risk assessment, provide the process flow diagram of transformer health control algorithm below as shown in Figure 6.

First the present embodiment carries out health evaluating by existing appraisal procedure to transformer, obtains the health index of transformer, and calculates probability of malfunction by health index.Then estimate in conjunction with breakdown loss, risk assessment is carried out to transformer, calculates current risk cost.Subsequently according to size and the Comment gathers formulation maintenance order of risk cost, meanwhile, set up mathematical model of optimization in conjunction with risk cost and the cost of overhaul, find best maintenance mode.Finally obtain the repair based on condition of component strategy under normal operation.

In several embodiments that the application provides, should be understood that disclosed equipment and method can realize by another way.Apparatus embodiments described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, and as: multiple unit or assembly can be in conjunction with, maybe can be integrated into another system, or some features can be ignored, or do not perform.In addition, the coupling each other of shown or discussed each ingredient or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of equipment or unit or communication connection can be electrical, machinery or other form.

The above-mentioned unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, also can be distributed in multiple network element; Part or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.

In addition, each functional unit in various embodiments of the present invention can all be integrated in a processing module, also can be each unit individually as a unit, also can two or more unit in a unit integrated; Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form that hardware also can be adopted to add SFU software functional unit realizes.

One of ordinary skill in the art will appreciate that: all or part of step realizing said method embodiment can have been come by the hardware that programmed instruction is relevant, aforesaid program can be stored in a computer read/write memory medium, this program, when performing, performs the step comprising said method embodiment; And aforesaid storage medium comprises: movable storage device, ROM (read-only memory) (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disc or CD etc. various can be program code stored medium.

The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; change can be expected easily or replace, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.

Claims (10)

1. an electric network state method for supervising, is characterized in that, described method comprises:

Obtain electric network state parameter;

Localized network intelligent body is built according to described electric network state parameter;

Global network intelligent body is built based on described localized network intelligent body;

By described global network intelligent body to the monitor and forecast of electric network state.

2. method according to claim 1, is characterized in that, described acquisition electric network state parameter comprises:

Obtain the operational factor of the designated equipment of described electrical network; Described operational factor is sent to the data processing unit of the transformer station at described designated equipment place.

3. method according to claim 2, is characterized in that, describedly builds localized network intelligent body according to described electric network state parameter and comprises:

Analysis is carried out to described electric network state parameter and obtains grouped data;

The localized network intelligent body described transformer station being carried out to state estimation is configured for according to described grouped data.

4. method according to claim 1, is characterized in that, describedly builds global network intelligent body based on described localized network intelligent body and comprises:

Set up the data channel between described localized network intelligent body;

By described data channel by described localized network intelligent body composition global network intelligent body.

5. method according to claim 1, is characterized in that, is describedly comprised by the monitor and forecast of described global network intelligent body to electric network state:

Described global network intelligent body carries out analysis to described electric network state parameter and obtains state estimation information;

The control strategy of operation of power networks requirement is met according to described state estimation information;

Described control strategy is sent to the transformer station that described localized network intelligent body is corresponding;

Described transformer station adjusts grid equipment according to described control strategy.

6. an electric network state supervisory system, is characterized in that, described system comprises:

Data acquisition facility, for obtaining electric network state parameter;

Local data's construction device, for building localized network intelligent body according to described electric network state parameter;

Global data construction device, for building global network intelligent body based on described localized network intelligent body;

State monitoring device, for passing through described global network intelligent body to the monitor and forecast of electric network state.

7. method according to claim 6, is characterized in that, described data acquisition facility comprises:

Sensor, for obtaining the operational factor of the designated equipment of described electrical network;

Wireless launcher, for being sent to the data processing unit of the transformer station at described designated equipment place by described operational factor.

8. method according to claim 6, is characterized in that, described local data construction device comprises:

Data processing unit, obtains grouped data for carrying out analysis to described electric network state parameter;

Localized network construction unit, for being configured for the localized network intelligent body described transformer station being carried out to state estimation according to described grouped data.

9. method according to claim 6, is characterized in that, described global data construction device comprises:

Path Setup unit, for setting up the data channel between described localized network intelligent body;

Global network construction unit, for forming global network intelligent body by described data channel by described localized network intelligent body.

10. method according to claim 6, is characterized in that, described state monitoring device comprises:

Data analysis unit, obtains state estimation information for carrying out analysis by described global network intelligent body to described electric network state parameter;

Strategy generating unit, for being met the control strategy of operation of power networks requirement according to described state estimation information;

Information transmitting unit, for sending to described control strategy the transformer station that described localized network intelligent body is corresponding.