CN105913177A - Scheduling power failure plan information processing method based on cloud - Google Patents

Abstract

The present invention relates to a cloud-based dispatching power outage plan information processing method, including the following steps: (1): evaluation of power outage willingness: calculating power outage necessity index and maintenance unit willingness index according to dispatching power outage plan information; step (2): weak mode Checking: Judging the load power changes of the systems involved in the dispatching power outage plan information before and after the power outage, and then detecting other risk factors related to the dispatching power outage plan information; Step (3): Evaluation of the power outage plan information level: the dispatching power outage Plan information and its corresponding risk factor information are used for reasoning, judgment and decision-making, so as to classify and sort the dispatched outage plan information; Step (4): Intelligent arrangement of outage plan information: use algorithms to generate outage plans that meet risk and safety constraints Arranged by the shipping party. The invention can solve the current problem of large personnel workload, and further improve the working efficiency of personnel and the safety of the power grid.

Description

Information processing method of dispatching outage plan based on cloud

technical field

The invention belongs to the technical field of electric power systems, and in particular relates to a method for processing information of a dispatching power outage plan based on a cloud.

Background technique

In recent years, the scope of dispatching power outage planning has become wider and wider. Power outage work is increasing day by day, and with the subdivision of professional institutions, the work surface that needs to be coordinated becomes wider. Dispatching balances more than 600 power outage plans on average every month, covering a wide range of areas, and there is no horizontal connection between power outage units. At the same time, the workload of power outage planning is increasing day by day, but the number of transport personnel has not increased. It is necessary to propose a method to reduce the workload of transport personnel. The improvement of power outage work efficiency is imminent, and the safety also needs to be improved. At present, the management of power outage plans in prefecture-level cities in China mostly adopts the method of reporting power outage plans by lower-level dispatching and maintenance units, and the dispatchers at the same level conduct manual analysis and balance, and hold power outage meetings to determine. On the whole, at present, there is still a lack of a system for rating the power outage willingness of maintenance units in China, and there is a lack of horizontal connections between maintenance units. At the same time, there is still a lack of a system for automatic identification of power outage plans, risk level evaluation system, and a comprehensive evaluation system for dispatching power outage plan management systems based on the level of maintenance willingness of the maintenance unit. Therefore, it is very meaningful to study the information processing method of dispatching outage plan based on cloud.

Contents of the invention

The purpose of the present invention is to provide a kind of.

In order to achieve the above object, the technical scheme adopted in the present invention is:

A cloud-based dispatching power outage plan information processing method is used to realize the coordinated processing of dispatching power outage plan information, and the cloud-based dispatching power outage plan information processing method includes the following steps:

Step (1): Evaluation of power outage willingness:

Calculating a power outage necessity index and a maintenance unit willingness index according to the dispatching power outage plan information, and judging accordingly whether it is necessary to carry out the power outage maintenance items involved in the dispatching power outage plan information;

Step (2): Weak way check:

Judging the load power changes of the system involved in the dispatched power outage plan information before and after the power outage, and then detecting other risk factors related to the dispatched power outage plan information, and saving the load power change and risk factor information ;

Step (3): Evaluation of blackout plan information level:

Perform reasoning, judgment and decision-making on the dispatched power outage plan information and its corresponding risk factor information, thereby classifying and sorting each of the dispatched power outage plan information;

Step (4): Intelligent arrangement of outage plan information:

Based on the grading and sorting of each dispatched outage plan information, an algorithm is used to generate a power outage plan that satisfies risk and safety constraints.

In the step (1), the power outage necessity index N ₁ =-log(t ₁ -t ₂ )-L, wherein, t ₁ is the longest allowable interval of the maintenance time of the system involved in the dispatched power outage plan information , t ₂ is the time interval from the last maintenance of the system involved in the scheduled power outage plan information, and L is the current load level of the system involved in the dispatched power outage plan information.

In the step (1), the willingness index of the maintenance unit N ₂ =-M+W, where M is the number of maintenance completed by the maintenance unit this month, and W is the subjective willingness of the maintenance unit.

In the step (2), the change of the load power is analyzed based on a directed graph composed of devices as nodes and power flow relationships between devices as edges.

In the step (2), the power flow relationship between devices in the system before and after the power outage is generated by an algorithm based on the directed graph before and after the power outage, starting from the root node of each tree in the directed graph after the power outage Do depth-first traversal, and compare the directed graph after the power failure with the directed graph before the power failure at each node, so as to obtain the load power changes of each device in the system before and after the power failure.

In the step (3), an expert knowledge base capable of simulating the decision-making process of human experts is established to automatically perform the reasoning, judgment and decision-making.

In the step (4), through N-1 scanning and topology comparison, a greedy algorithm and a dynamic programming algorithm are used to generate the operator arrangement of the power outage plan.

Due to the application of the above-mentioned technical solutions, the present invention has the following advantages compared with the prior art: the present invention can realize the reporting of the power outage plan, horizontal coordination processing, safety check, and intelligent operator arrangement for power outage maintenance under the premise of ensuring the information security of the power grid , It can solve the problem that the full-time personnel of the dispatching and maintenance departments at all levels currently have a large workload in formulating dispatching monthly power outage plans, and further improve the work efficiency of personnel and the security of the power grid.

Description of drawings

Accompanying drawing 1 is the overall framework of the system implementing the present invention.

detailed description

The present invention will be further described below in conjunction with embodiment.

Embodiment 1: A cloud-based dispatching power outage plan information processing method for realizing coordinated processing of dispatching power outage plan information, comprising the following steps:

Step (1): Evaluation of Power Outage Willingness

Calculate outage necessity index and maintenance unit willingness index according to dispatching outage plan information, and judge whether to carry out outage maintenance items involved in dispatching outage plan information.

Power outage necessity index N ₁ =-log(t ₁ -t ₂ )-L, where t ₁ is the longest allowable interval of maintenance time of the systems involved in the dispatched power outage plan information, and t ₂ is the distance of the systems involved in the dispatched power outage plan information The time interval of the last overhaul, L is the current load level of the system involved in the scheduling power outage plan information. The necessity of power outage is positively correlated with the time interval from the last maintenance, and negatively correlated with the longest allowable interval of maintenance time and system load level. When the time interval from the last inspection reaches the maximum allowable interval, a power outage inspection must be carried out. Maintenance unit willingness index N ₂ =-M+W, where M is the number of maintenance completed by the maintenance unit this month, and W is the subjective willingness of the maintenance unit.

Step (2): Weak way check

Judging the load power change of the system involved in the dispatching blackout plan information before and after the power outage, and then detecting other risk factors related to the dispatching blackout plan information, and saving the load power change and risk factor information.

Specifically, the load power variation is analyzed based on a directed graph composed of two (multiple) terminal devices as nodes and power flow relationships between devices as edges. First, analyze the equipment overload, and generate a power outage based on the directed graph before and after the power outage and through an algorithm (for example, using the algorithm disclosed in the invention patent "Identification Method for Weak Links of Distribution Network Based on Equipment Power Flow Topology" with application number 201510049843.5) The power flow relationship between the devices in the front and rear systems, and then do a depth-first traversal (DFS) from the root node of each tree in the directed graph after the power outage, and compare the directed graph after the power outage with the before power outage at each node By comparing the directed graph of each device, the load power change of each device in the system before and after the power failure can be obtained by comparing the power flow relationship of each device. After that, other risk factors are detected, so as to analyze the weak operation mode in the network (including single main transformer operation mode, series supply mode, etc.). Risk factor information resulting from the analysis will be stored in the system database for subsequent processes in the system.

Step (3): Evaluation of blackout plan information level

Execute the aforementioned load rate and weak mode check and analysis algorithm for each scheduled power outage plan information, and record the risk information corresponding to each dispatched power outage plan, and save it in the database. Establish a set of expert knowledge base in the system that can simulate the decision-making process of human experts, so that the system can reason, judge and make decisions on dispatching power outage plan information and its corresponding risk factor information, and classify each dispatching power outage plan information by quantitative means and sort them.

Step (4): Intelligent Arrangement of Outage Plan Information

Based on the grading and sorting of each dispatching outage plan information, through N-1 scanning and topology comparison, the greedy algorithm and dynamic programming algorithm are used to generate the optimal and suboptimal outage plan arrangements that meet the risk and safety constraints.

The above-mentioned cloud-based scheduling power outage plan information processing method is implemented by the system software as shown in FIG. 1 .

Take the lines and transformers of a 110kV station in a power grid in a certain area as an example for analysis. Before the optimization of the cloud-based scheduling outage planning processing method, the planned outage time of the line connected to the 110kV bus of the station is from June 5th to June 13th, and the planned outage time of the transformer connected with this line is From June 1st to June 3rd. Therefore, without considering the power outage of other equipment, the load on this line will be out of power for 12 days.

The analysis is carried out using the cloud-based dispatching power outage plan processing system. The longest allowable interval for line maintenance is 30 days, the time interval from the last maintenance is 20 days, and the load level is 1, so the necessity of power outage: N ₁ =-log (t ₁ -t ₂ )-L=-2. The number of overhauls completed by the maintenance unit this month is 15, the degree of subjective willingness of the maintenance unit is 30, and the willingness of the maintenance unit is expressed as: N ₂ =-M+W=15. The longest interval between transformer inspections is 1 year, the interval from the last inspection is half a year, and the load level is 1, so the necessity of power failure: N ₁ =-log(t ₁ -t ₂ )-L=-3.26. The degree of subjective willingness of the maintenance unit is 30, and the willingness of the maintenance unit is expressed as: N ₂ =-M+W=15. When the power outage necessity index N ₁ >Th ₁ , or the maintenance unit willingness index N ₂ >Th ₂ , maintenance is required, and the thresholds Th ₁ and Th ₂ are artificially set by the user based on expert experience. In this example, Th ₁ =-5 and Th ₂ =10 are taken, so both the line and the transformer need to be powered off for maintenance.

Through the technical analysis of the intelligent operator arrangement technology for checking and dispatching the power outage plan in a weak way, the lines and transformers can be packaged for power outage at the same time, and the safe operation requirements of the system can be met. Through N-1 scanning and topology comparison, the system adopts greedy algorithm and dynamic programming algorithm to generate an optimal power outage plan that satisfies risk and safety constraints. The power outage of the transformer will be carried out from the 5th to the 8th of June.

Comparing the system before and after optimization, before optimization, the load of this line would cause 12 days of power outage due to lines and transformers; after optimization, the number of days of power outages caused by lines and transformers was shortened to 9 days. The reliability of power supply is improved.

The present invention sorts each power outage scheduling plan to be investigated through the operation of the power outage willing evaluation system, load rate and weak mode checking technology, and dispatching power outage plan risk analysis and grade evaluation system. Through N-1 scanning and topology comparison, the system adopts greedy algorithm and dynamic programming algorithm to sequentially generate the optimal and suboptimal power outage plan arrangements that meet the risk and safety constraints. Users can adjust the weight of each of the above evaluation indicators to meet the priority level of specific needs. It can solve the problem of large workload of full-time personnel in dispatching and maintenance departments at all levels to formulate dispatching monthly power outage plans, and further improve the work efficiency of personnel and the safety of the power grid.

The above-mentioned embodiments are only to illustrate the technical concept and characteristics of the present invention, and the purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly, and not to limit the protection scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention shall fall within the protection scope of the present invention.

Claims (7)

1. a scheduling power failure plan information processing method based on cloud, is used for realizing dispatching power failure plan letter Breath Coordination Treatment, it is characterised in that: described scheduling power failure plan information processing method based on cloud include with Lower step:

Step (1): power-cut wish evaluation:

Power failure necessity index and overhaul unit wish index is calculated according to described scheduling power failure plan information, and Judge whether the interruption maintenance project needing to carry out relating in described scheduling power failure plan information accordingly；

Step (2): weak mode is checked:

Judge the system related in described scheduling power failure plan information bearing power change feelings before and after having a power failure Condition, then the risk factor that other is relevant with described scheduling power failure plan information are detected, and preserve institute of institute State bearing power situation of change and risk factor information；

Step (3): power failure plan information grade is evaluated:

The risk factor information of described scheduling power failure plan information and correspondence thereof is made inferences judgement and decision-making, Thus each described scheduling power failure plan information is carried out classification and sequence；

Step (4): the intelligence arrangement of power failure plan information:

Based on the classification of scheduling power failure plan information each described and sequence, adopt be generated algorithmically by meet risk with The power failure plan fortune side of security constraint arranges.

Scheduling power failure plan information processing method based on cloud the most according to claim 1, its feature It is: in described step (1), described power failure necessity index N₁=-log (t₁-t₂)-L, wherein, t₁For Repair time the longest permission thing relate to system in described scheduling power failure plan information is spaced, t₂Stop for described scheduling Relating to the time interval of system distance maintenance last time in electricity plan information, L is described scheduling power failure plan information In relate to the current loads degree of system.

Scheduling power failure plan information processing method based on cloud the most according to claim 1, its feature It is: in described step (1), described overhaul unit wish index N₂=-M+W, wherein, M is inspection Repairing unit this month completed maintenance number, W is the subjective desire degree of overhaul unit.

Scheduling power failure plan information processing method based on cloud the most according to claim 1, its feature It is: in described step (2), based on using equipment as node, with the flow of power relation between equipment For bearing power situation of change described in the Directed Graph analysis that limit is constituted.

Scheduling power failure plan information processing method based on cloud the most according to claim 4, its feature Be: in described step (2), based on have a power failure before and after described directed graph and by algorithm generate power failure before Flow of power relation between rear devices in system, the root node of each tree the described directed graph after having a power failure Start to do depth-first traversal, and have described in before the described directed graph after each node will have a power failure and power failure Compare to figure, thus draw each equipment bearing power situation of change before and after having a power failure in system.

Scheduling power failure plan information processing method based on cloud the most according to claim 1, its feature Be: in described step (3), set up can simulating human expert decision-making process expert knowledge library and from Move and carry out described reasoning and judging and decision-making.

Scheduling power failure plan information processing method based on cloud the most according to claim 1, its feature It is: in described step (4), is compared by N-1 scanning and topology, use greedy algorithm and dynamic rule The method of calculating generates the described plan fortune side that has a power failure and arranges.