CN113315116A - Operation optimization method for incremental distribution network equipment - Google Patents
Operation optimization method for incremental distribution network equipment Download PDFInfo
- Publication number
- CN113315116A CN113315116A CN202010782722.2A CN202010782722A CN113315116A CN 113315116 A CN113315116 A CN 113315116A CN 202010782722 A CN202010782722 A CN 202010782722A CN 113315116 A CN113315116 A CN 113315116A
- Authority
- CN
- China
- Prior art keywords
- equipment
- state
- distribution network
- cost
- incremental distribution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000005457 optimization Methods 0.000 title claims abstract description 6
- 230000008439 repair process Effects 0.000 claims description 35
- 238000012423 maintenance Methods 0.000 claims description 12
- 238000007689 inspection Methods 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 230000015556 catabolic process Effects 0.000 description 8
- 238000006731 degradation reaction Methods 0.000 description 8
- 230000007704 transition Effects 0.000 description 7
- 238000013486 operation strategy Methods 0.000 description 6
- 230000006866 deterioration Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012546 transfer Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004904 shortening Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- GMTYREVWZXJPLF-AFHUBHILSA-N butorphanol D-tartrate Chemical compound OC(=O)[C@@H](O)[C@H](O)C(O)=O.N1([C@@H]2CC3=CC=C(C=C3[C@@]3([C@]2(CCCC3)O)CC1)O)CC1CCC1 GMTYREVWZXJPLF-AFHUBHILSA-N 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of incremental distribution networks. The invention provides an operation optimization method of incremental distribution network equipment, which comprises the following steps: calculating the reliability parameters of the equipment, and determining the state probability results of 4 lines of the incremental distribution network system; and calculating the optimal equipment operation state of each line based on the state probability result of the line. The method has obvious effect of improving the reliability of the equipment, and has obvious improvement effect of over 70 percent on EENS indexes of equipment operation. Meanwhile, the power failure loss of the system can be obviously reduced when the equipment operates, and the normal stable operation and the power supply reliability of the incremental distribution network system are integrally improved.
Description
Technical Field
The invention relates to the technical field of incremental distribution networks.
Background
In the incremental distribution network system, the devices are various and large in number, which brings great difficulty to the operation and maintenance of the devices. One of important services for ensuring the normal and stable operation and the power supply reliability of the incremental distribution network. How to make different operation strategies for various types of equipment and set the optimal operation time and operation mode according to the importance degree of the equipment in the system is a key difficult problem of continuous research in the field.
Disclosure of Invention
The invention discloses an operation optimization method of incremental distribution network equipment, which comprises the following steps: calculating the reliability parameters of the equipment, and determining the state probability results of 4 lines of the incremental distribution network system; and calculating the optimal equipment operation state of each line based on the state probability result of the line.
Further, the state probability result includes: minor repairs, major repairs, failure or outage probability.
Further, the optimal device operation state refers to a normal operation state S that the device keeps the best0。
Further, the method for calculating the optimal device operating state comprises the following steps:
min C＝min(CEENS+CM)
wherein:
in the formula: cEENSLoss due to power failure; k is a radical ofEENSThe power failure loss coefficient; the EENS system stops electricity every year; cMThe operation cost comprises inspection cost, minor repair cost, major repair cost and fault maintenance cost; d1、Dmin or、Dmajor、DFThe time lengths of inspection, minor repair, major repair and fault maintenance are respectively; k is a radical of1、kmin orThe cost coefficients of the time duration are respectively.
The method has obvious effect of improving the reliability of the equipment, and has obvious improvement effect of over 70 percent on EENS indexes of equipment operation. Meanwhile, the power failure loss of the system can be obviously reduced when the equipment operates, and the normal stable operation and the power supply reliability of the incremental distribution network system are integrally improved.
Drawings
Fig. 1 is a diagram of incremental distribution network equipment.
FIG. 2 is a diagram of an incremental distribution equipment state model.
Fig. 3 is an operation diagram of an incremental distribution network device.
Detailed Description
Example 1
Taking RBTS-Bus6 incremental distribution as an example, as shown in FIG. 1.
The incremental distribution network has 4 overhead feeders in total.
The fault rate of the incremental distribution network line is 0.065 times/km/year. The deterioration of the line is divided into two stages, the deterioration rate of the first stage is 0.1 times/km/year, and the deterioration rate of the second stage is 0.3 times/km/year if no maintenance measures are taken. If no maintenance measures are taken, the transition rate of the line from the second deteriorated state to the failed state is 0.5 times/km/year. The cost of inspection, minor repair and major repair is respectively 50, 100 and 200 yuan/h/km. The time length of minor repair and major repair is 1 and 2h/km respectively. The power outage loss of EENS is 10 yuan/(kWh).
And calculating the reliability parameters of the equipment, and determining the probability that the state probability result of the 4 lines of the incremental distribution network system is minor repair, major repair, failure or outage.
Calculating device reliability parameters, and writing the state probability of each device into a column vector X ═ X based on the state transition diagram0，X1，X2，XF，X10，X11，X12]In combination with the Markov state transition matrix, the available equations are specifically as follows:
wherein:
after the probability of occurrence of each state is obtained, the frequency F ═ F of occurrence of each state can be calculated0，F1，F2，FF，F10，F11，F12]The method comprises the following steps:
from the above formula, λ1Frequency of failure F of the apparatus at infinite increaseFWill approach to λ in its numerator1The ratio between the coefficients of the highest order term (cubic term) is λ01λ12λ2F/μ110μF0. Therefore, it is known that shortening the polling interval can not lower the failure rate of the equipment infinitely, and conversely, after shortening the polling interval, the frequency of minor repair and major repair of the equipment is increased, which in turn leads to an increase in the overall outage rate of the equipment and may adversely affect the overall reliability of the system. Therefore, merely building a multi-state transition model for a single device does not fully solve the state-run optimization problem.
Through a calculation formula of the reliability parameters, the state probabilities of the four lines can be obtained as follows:
TABLE 1
And calculating the power failure loss and the operation cost of each line based on the state probability result of the line, and determining the optimal equipment operation state of each line by taking the minimum power failure loss and operation cost as a target.
Determining the optimal equipment running state according to the following formula:
min C＝min(CEENS+CM)
wherein:
in the formula: cEENSLoss due to power failure; k is a radical ofEENSThe power failure loss coefficient; the EENS system stops electricity every year; cMThe operation cost comprises inspection cost, minor repair cost, major repair cost and fault maintenance cost; d1、Dmin or、Dmajor、DFThe time lengths of inspection, minor repair, major repair and fault maintenance are respectively; k is a radical of1、kmin orThe cost coefficients of the time duration are respectively.
The optimal operation method of each line is as follows:
TABLE 2
The optimal operation modes of the 4 feeders are the strategies (1) and (3), and the operation mode of the strategy is to detect the degradation state S1Then minor repair is performed and the deterioration state S is detected2Overhauling to maintain the equipment in the optimal normal running state S0。
On the basis of the Markov chain, an incremental power distribution equipment state model is constructed, and is specifically shown in FIG. 2.
S1And S2Representing 2 degradation states of the power distribution equipment, which respectively correspond to an abnormal state and a serious state defined in the operation and maintenance of an actual power grid; i is0、I1、I2Then the representative device is in a check state; lambda [ alpha ]01、λ12、λ2F、λ1、μ100、μ110、μ120、μF0The state transition rates of the devices are all the unit of times/year; lambda [ alpha ]01For devices from S0To S1The transfer rate of (a); lambda [ alpha ]12Is a slave S1To S2Is transferred toRate; lambda [ alpha ]2FIs a slave S2To SFThe transfer rate of (a); mu.sF0Is a slave SFTo a normal state S0The transfer rate of (a); lambda [ alpha ]1The polling rate of the equipment represents the polling frequency of the equipment; mu.s100And the time length of the equipment is counted down to finish the inspection.
The state of the equipment can be returned to the previous degradation state through minor repair, wherein one operation mode is minor repair. I is1To S0The process of (2) is a minor repair process; mu.s110Representing the minor repair completion rate, and taking the value as the reciprocal of the minor repair duration; the other is overhaul, and the state of the equipment can be returned to a normal state through overhaul. I is2To S0The process of (2) is the overhaul process. Mu.s120Representing the overhaul completion rate, and taking the value as the reciprocal of the overhaul duration.
The state transition process of the device in the operation process can be simply described as follows: operator according to lambda1The frequency of the device is regularly checked, when the device is checked, if the device is found to be normal, the device does not run, and if the device is in an abnormal state S1And then, taking minor repair measures to enable the equipment to recover the normal state. If the device is in a critical state S2And adopting major repair measures to still enable the equipment to recover the normal state. After the operation strategy is considered, the time for the equipment to reach the fault state is delayed, and the reliability is improved.
Therefore, the operation strategy needs to be further optimized on the whole, and the decision variables are the routing inspection interval and the operation strategy. The polling interval is a continuous real number which is greater than 0 and can be represented by I; the operating strategy is a discrete decision variable. For a device state markov model with 2 degradation states, there may be 4 operating strategies, as shown in fig. 3 in particular below.
The state transition process identified by 4 arrows has 4 operation strategy combinations in total, and the corresponding decision vector s is ═ s10，s11，s20，s21]And the value of the element in the vector is 0 or 1. When the inspection personnel find that the equipment is degraded, 4 decision variables take different values, and the following 4 operation strategies are available:
1): (1) and (3) strategy, s ═ 1, 0, 1, 0]Detecting the degradation state S1Then minor repair is performed and the deterioration state S is detected2Overhauling to maintain the equipment in the optimal normal running state S0。
2): (1) and (4) strategy, s ═ 1, 0, 0, 1]Detecting the degradation state S1And S2And (5) slightly repairing to make the equipment return to the original state.
3): (2) and (3) strategy, s ═ 0, 1, 1, 0]Detecting the degradation state S1Without maintenance, only the degraded state S being detected2After major repair, the equipment is recovered to normal S0Status.
4) Strategies (2) and (4), s ═ 0, 1, 0, 1]Detecting the degradation state S1Without maintenance, only the degraded state S being detected2After minor repair, the equipment is returned to S1A degraded state.
The method has obvious effect of improving the reliability of the equipment, and has obvious improvement effect of more than 70 percent on the FFNS index of the running of the equipment. Meanwhile, the power failure loss of the system can be obviously reduced when the equipment operates, and the normal stable operation and the power supply reliability of the incremental distribution network system are integrally improved.
Claims (4)
1. An operation optimization method for incremental distribution network equipment is characterized by comprising the following steps:
calculating the reliability parameters of the equipment, and determining the state probability results of 4 lines of the incremental distribution network system;
and calculating the optimal equipment operation state of each line based on the state probability result of the line.
2. The method of claim 1 wherein said state probability results comprise: minor repairs, major repairs, failure or outage probability.
3. The method of claim 1, wherein the optimal operating condition of the plant is a normal operating condition S in which the plant is maintained at an optimal level0。
4. The method of claim 1, wherein the optimal plant operating conditions are calculated by:
min C＝min(CEENS+CM)
wherein:
in the formula: cEENSLoss due to power failure; k is a radical ofEENSThe power failure loss coefficient; the EENS system stops electricity every year; cMThe operation cost comprises inspection cost, minor repair cost, major repair cost and fault maintenance cost; d1、Dminor、Dmajor、DFThe time lengths of inspection, minor repair, major repair and fault maintenance are respectively; k is a radical of1、kminorThe cost coefficients of the time duration are respectively.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010782722.2A CN113315116A (en) | 2020-08-06 | 2020-08-06 | Operation optimization method for incremental distribution network equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010782722.2A CN113315116A (en) | 2020-08-06 | 2020-08-06 | Operation optimization method for incremental distribution network equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113315116A true CN113315116A (en) | 2021-08-27 |
Family
ID=77370362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010782722.2A Pending CN113315116A (en) | 2020-08-06 | 2020-08-06 | Operation optimization method for incremental distribution network equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113315116A (en) |
-
2020
- 2020-08-06 CN CN202010782722.2A patent/CN113315116A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108335021B (en) | Method for evaluating state reliability of wind turbine and maintenance decision optimization | |
| CN104517241B (en) | A kind of methods of risk assessment based on transmission line of electricity full working scope information | |
| CN110619386B (en) | TMR operation monitoring and fault intelligent research and judgment method and system | |
| CN103914788B (en) | The reliability estimation method of power grid multilink system | |
| CN106447201B (en) | The best maintenance decision method of power transmission and transforming equipment based on Markovian decision process | |
| CN110287543B (en) | Method for predicting service life of relay protection device | |
| CN111444593B (en) | Method for improving vulnerability of elements of electricity-gas comprehensive energy system | |
| CN113762604B (en) | Industrial Internet big data service system | |
| CN106875127B (en) | Unified power flow controller reliability modeling and access power grid reliability assessment method thereof | |
| CN110763953B (en) | Troubleshooting line patrol path planning method under distribution automation condition | |
| CN110930072A (en) | Reliability research method of traction power supply system | |
| CN105528742A (en) | Circuit breaker failure probability assessment method | |
| CN104680239A (en) | Distribution network maintenance scheme optimization method and device based on element failure model | |
| CN106919984A (en) | Parallel system Repairable Unit repair determining method based on cost | |
| CN113315116A (en) | Operation optimization method for incremental distribution network equipment | |
| CN113094920A (en) | Power distribution network reliability weak link analysis method based on fault consequence analysis expression | |
| CN117236030A (en) | Power system toughness evaluation modeling method considering cascading overload fault occurrence under typhoon disaster | |
| CN109936134B (en) | Method and device for quickly identifying power limit interval of thermally stable power transmission channel | |
| CN112332420A (en) | Device and method for determining hierarchical load reduction in power system risk assessment | |
| CN115237081B (en) | Method, apparatus, and medium for determining post-processing device with exception | |
| CN108875205B (en) | System availability efficient simulation method based on reachable matrix and discrete event driving | |
| CN111208386A (en) | Power distribution network fault rapid positioning method adaptive to topology change and information error | |
| CN107909161B (en) | Traction power supply equipment maintenance method based on risk cost prediction | |
| CN107546742B (en) | Day-ahead planned power flow active power and voltage calculation error analysis method | |
| CN109473977B (en) | Risk-considering rapid prevention control method for power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210827 |
|
| WD01 | Invention patent application deemed withdrawn after publication |