CN116436162B - Supervision analysis system for distribution network feeder line fault self-healing processing - Google Patents
Supervision analysis system for distribution network feeder line fault self-healing processing Download PDFInfo
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- CN116436162B CN116436162B CN202310430676.3A CN202310430676A CN116436162B CN 116436162 B CN116436162 B CN 116436162B CN 202310430676 A CN202310430676 A CN 202310430676A CN 116436162 B CN116436162 B CN 116436162B
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- 230000007613 environmental effect Effects 0.000 claims abstract description 38
- 238000010276 construction Methods 0.000 claims abstract description 16
- 238000013507 mapping Methods 0.000 claims description 27
- 238000001514 detection method Methods 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000007665 sagging Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
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- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H1/00—Details of emergency protective circuit arrangements
- H02H1/0007—Details of emergency protective circuit arrangements concerning the detecting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H1/00—Details of emergency protective circuit arrangements
- H02H1/0061—Details of emergency protective circuit arrangements concerning transmission of signals
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- 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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a supervision and analysis system for self-healing processing of distribution network feeder line faults, belongs to the field of self-healing supervision of distribution network feeder line faults, and solves the problem of how to analyze the capacity of self-healing processing of the distribution network feeder line faults by combining environmental influence factors so as to timely early warn processing failure points which cannot be self-healed due to the environmental influence factors; the information classification module can judge whether the power failure of each distribution network feeder line section in the district is fault power failure in a corresponding time period; the self-healing association module can find out the influence of environmental influence factors on the self-healing processing of the distribution network feeder line, and the environmental early warning module is combined to timely early warn failure points which cannot be subjected to the self-healing processing due to the environmental influence factors, so that the processing efficiency of failure problems due to the environmental factors is improved; the map construction module can intuitively display the fault points, and related staff can perform corresponding early warning processing at the first time.
Description
Technical Field
The invention belongs to the field of self-healing supervision of distribution network feeder line faults, and particularly relates to a supervision analysis system for self-healing processing of distribution network feeder line faults.
Background
A feeder is a term in a power distribution network, and may refer to a branch connected to any distribution network node, and may be a feed-in branch or a feed-out branch. But because the typical topology of the distribution network is radial, the energy flow in most feeders is unidirectional. We can feed power to the opposite end through the feeder. When the distribution feeder line fails, the fault element can be removed by the self-healing processing technology, and the affected sound area power supply can be quickly restored with little or no human intervention, so that the power supply service to the user is hardly interrupted.
When the distribution network feeder line fault self-healing processing technology is utilized to process fault line sections, hundred percent of automatic fault isolation cannot be achieved, and more influence factors influence the distribution network feeder line fault self-healing, wherein the environmental influence factors are larger. The capacity of self-healing processing of the distribution network feeder line faults is analyzed by combining environment influence factors temporarily, so that early warning processing is performed on fault points which cannot be self-healed due to the environment influence factors in time. Therefore, the invention provides a supervision analysis system for self-healing processing of distribution network feeder line faults.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a supervision and analysis system for self-healing processing of distribution network feeder faults, which solves the problem of how to analyze the capacity of self-healing processing of the distribution network feeder faults by combining environmental influence factors, thereby carrying out early warning processing on fault points which cannot be self-healed due to the environmental influence factors in time.
To achieve the above object, an embodiment according to a first aspect of the present invention provides a supervision and analysis system for self-healing processing of a distribution network feeder line fault, including: the system comprises an information acquisition module, an information classification module, a self-healing association module, an environment early warning module and a map construction module;
the information acquisition module is used for acquiring power information, environment information and position information of the distribution network feeder line in the district and uploading the power information, the environment information and the position information to the cloud platform module;
the information classification module is used for analyzing the power information of the distribution network feeder lines in the jurisdiction so as to classify whether the power failure occurs to each distribution network feeder line section in the jurisdiction or not and upload the power failure to the cloud platform module;
the self-healing association module is used for analyzing the power information of the distribution network feeder line section and the corresponding environment information due to failure outage, establishing a mapping association table of the environment influence and uploading the mapping association table to the cloud platform module;
the environment early warning module is used for carrying out early warning on whether the current distribution network feeder can carry out fault self-healing according to the mapping association table of the environment information and the environment influence of the current distribution network feeder detected in real time, and sending the position information and the early warning signal of the environment detection to the map construction module;
the map construction module is used for constructing an electronic map according to the position information of distribution network feeder lines in the jurisdiction, marking the acquired position information and early warning signals of environment detection on the electronic map, uploading the position information and the early warning signals to the cloud platform module, and preventing and processing by staff.
Further, the power information of the distribution network feeder line in the district comprises current, voltage and on-off state of each distribution network feeder line section; the environment information of the distribution network feeder lines in the jurisdiction comprises the arc sagging, the galloping, the vibration, the temperature and the relative air humidity of the environment where each distribution network feeder line section is located and the lightning intensity suffered.
Further, the process of analyzing the power information of the distribution network feeder line in the district by the information classification module is as follows:
step S1: extracting power information in a history year from the cloud platform module, and numbering each distribution network feeder line section according to the primary and secondary orders of the distribution network feeder; screening the distribution network feeder line sections which are not powered off and not powered back in the same time period T, and storing the numbers of the distribution network feeder line sections into a power-off set of the time period T;
step S2: analyzing whether each numbered distribution network feeder line section in the power-off set of the T time period is a fault power-off or not in sequence;
intercepting r current discrete values Itij and r voltage discrete values Utij in a t time interval before power-off of a distribution network feeder line section with the number of i; i represents the number of the distribution network feeder line section, i=1, 2 … … i; j represents a time point within a t time interval;
step S3: comparing r current discrete values Itij in the t time interval with normal current range values (Ii 1, ii 2), respectively; and comparing r voltage discrete values Utij in the t time interval with normal voltage range values (Ui 1, ui 2) respectively;
if r current discrete values Itij all belong to (Ii 1, ii 2) and r voltage discrete values Utij all belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is non-fault power-off;
if one of the r current discrete values Itij does not belong to (Ii 1, ii 2) or/and one of the r voltage discrete values Utij does not belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is indicated to be fault power-off; storing the serial numbers of the distribution network feeder line sections with fault outage in the time T into a fault outage set in the time T;
step S4: and the information classification module sends the fault outage set of all time periods in one year to the cloud platform module for storage.
Further, in step S1, if the distribution network feeder line segments from the main line to the branch line of the distribution network feeder line are continuously powered off, the number of the distribution network feeder line segment closest to the power supply is selected and stored in the power-off set of the T time period; if the distribution network feeder line sections from the distribution network feeder line main line to the branch line are not continuously powered off, the numbers of the distribution network feeder line sections which are powered off are stored into a power-off set in the T time period.
Further, the normal current range value (Ii 1, ii 2) refers to a current range value which flows when the distribution network feeder line section numbered i has no fault; the normal voltage range values (Ui 1, ui 2) refer to the end voltage range values of the distribution network feeder line section numbered i when there is no fault.
Further, the self-healing association module analyzes the power information and the corresponding environment information of the distribution network feeder line section due to failure outage as follows:
step P1: extracting a fault outage set of a T time period from the cloud platform module, and extracting on-off state information of a corresponding distribution network feeder line section after the power is off in the T time period from the cloud platform module according to the serial numbers of the distribution network feeder line sections in the fault outage set of the T time period;
step P2: marking the distribution network feeder line sections which are electrified again after the power failure is carried out according to the distribution network feeder line fault self-healing technology in the T time period as self-healing electricity, and counting the number NT of the distribution network feeder line sections which are self-healing electricity in the T time period; marking distribution network feeder line sections which are not powered off according to a distribution network feeder fault self-healing technology in a T time period as manual re-electrification, and counting the number MT of the distribution network feeder line sections which are manually re-electrified in the T time period;
according to the calculation formula: ηT=NT/(NT+MT), and obtaining a self-healing proportionality coefficient ηT of the distribution network feeder line in the T time period;
step P3: extracting self-healing electricity environment information after fault outage in a period of T from a cloud platform module, wherein the environment information is sag HTk, galloping WTk, vibration ZTk, temperature DTk, relative air humidity STk and lightning intensity QTk; where k represents the number of detections at different time points in the T period, k=1, 2 … … k;
according to the calculation formula etk=a1×htk+a2× WTk +a3× ZTk +a4×dtk+a5×stk+a6× QTk; acquiring an environmental coefficient value ETk of a space where a distribution network feeder line at a k time point in a T time period is located, wherein a1, a2, a3, a4, a5 and a6 are preset proportion coefficient values;
step P4: arranging the environmental coefficient values ETk in the T time period in order from small to large, removing the maximum value and the minimum value, and forming the environmental coefficient range values (ET 1, ET 2) in the T time period by the remaining maximum environmental coefficient values and the remaining minimum environmental coefficient values;
step P5: mapping and correlating the self-healing proportionality coefficient eta T of the distribution network feeder line in the period of T with the environmental coefficient range values (ET 1 and ET 2); wherein T is a variable;
step P6: mapping and correlating the self-healing proportion coefficient of the distribution network feeder line and corresponding environment coefficient range values (ET 1, ET 2) in all time periods T in one year, establishing a mapping and correlating table of environmental influence, and sending the mapping and correlating table to the cloud platform module for storage.
Further, the early warning mode of the environment early warning module is as follows:
acquiring real-time environment information of a current distribution network feeder from a cloud platform module, and calculating an environment coefficient value;
comparing the calculated environment coefficient value with a mapping association table of the environment influence stored by the cloud platform module, and if the calculated environment coefficient value is in a corresponding environment coefficient range value, acquiring a self-healing proportionality coefficient;
if the current self-healing proportionality coefficient is larger than or equal to a preset self-healing proportionality coefficient threshold value, the environment early-warning module does not perform subsequent processing;
if the current self-healing proportionality coefficient is smaller than the preset self-healing proportionality coefficient threshold value, the environment early-warning module sends the position information of the environment detection and the early-warning signal to the map construction module.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention collects the power information, the environment information and the position information of the distribution network feeder line in the district through the information collection module; the method comprises the steps that the information classification module analyzes the power information of distribution network feeder lines in a district, so that whether power failure of each distribution network feeder line section in the district is a fault power failure in a corresponding time period can be judged, the number of each distribution network feeder line section with the fault power failure is counted, and basic data are provided for the analysis of a follow-up self-healing association module;
2. according to the invention, the self-healing association module analyzes the power information of the distribution network feeder line section which is powered off due to failure and the corresponding environment information, and a mapping association table of environmental influence is established, so that the influence of environmental influence factors on the self-healing processing of the distribution network feeder can be found, and the environment early warning module is combined to early warn whether the current distribution network feeder can perform fault self-healing according to the environment information of the distribution network feeder and the mapping association table of the environmental influence which are detected in real time currently; therefore, the fault points which cannot be subjected to self-healing treatment due to environmental influence factors can be timely early-warned, and the treatment efficiency of the fault problems due to the environmental influence factors is improved;
3. according to the invention, the map construction module is used for constructing the electronic map of the position information of the distribution network feeder line, and the acquired position information of the environment detection and the early warning signal are marked on the electronic map, so that the fault point can be intuitively displayed, and related staff can perform corresponding early warning processing at the first time.
Drawings
Fig. 1 is a block diagram of a system architecture of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a supervision and analysis system for self-healing processing of distribution network feeder line faults includes: the system comprises an information acquisition module, an information classification module, a self-healing association module, an environment early warning module, a map construction module and a cloud platform module; the modules are connected through an electrical and/or wireless network mode;
in the application, the information acquisition module is used for acquiring power information, environment information and position information of distribution network feeder lines in jurisdictions;
in a preferred embodiment, the power information of the distribution network feeder line in the district includes current, voltage and on-off state of each distribution network feeder line section;
a voltage and current detection sensor can be arranged at each distribution network node to detect the current flowing through the corresponding distribution network feeder line section and the terminal voltage of the distribution network feeder line section; the on-off of the distribution network feeder line section is detected by a switch connected with the corresponding distribution network feeder; wherein, the distribution network nodes are connected by distribution network feeder lines;
in a preferred embodiment, the environmental information of the space where the distribution network feeder line is located in the district includes arc sagging, galloping, vibration of each distribution network feeder line section, and temperature, relative air humidity, lightning intensity and the like of each distribution network feeder line section;
the arc sag detection sensor, the galloping detection sensor, the vibration detection sensor, the temperature and humidity detection sensor and the lightning intensity detection sensor can be arranged at each distribution network node, so that the arc sag, the galloping, the vibration, the temperature, the relative air humidity and the lightning intensity of a corresponding distribution network feeder line section are respectively detected;
in a preferred embodiment, the position information of the distribution network feeder line in the district is the geographic coordinates of each distribution network feeder line section;
positioning sensors can be arranged at the nodes of each distribution network, the geographic coordinates of the nodes of each distribution network of the distribution network in the district are obtained through a GPS positioning technology or a Beidou satellite positioning technology, the nodes of each distribution network are connected through distribution network feeder lines, and the geographic coordinates of the feeder line sections of each distribution network in the district are further obtained;
the information acquisition module transmits the acquired power information, environment information and position information of the distribution network feeder line in the district to the cloud platform module in real time in a wireless network mode.
In the application, the map construction module is used for constructing an electronic map according to the position information of the distribution network feeder line in the jurisdiction; uploading the constructed electronic map to a cloud platform module for storage;
specific procedures are known in the art and are not described herein.
In the application, the information classification module is used for analyzing the power information of the distribution network feeder lines in the jurisdiction so as to classify whether the power failure occurs to each distribution network feeder line section in the jurisdiction; the specific process is as follows:
step S1: the information classification module extracts power information in a history year from the cloud platform module, and numbers each distribution network feeder line section according to the primary and secondary orders of the distribution network feeder;
screening the distribution network feeder line sections which are not powered off and not powered back in the same time period T, and storing the numbers of the distribution network feeder line sections into a power-off set of the time period T;
if the distribution network feeder line sections from the main line to the branch line of the distribution network feeder line are continuously powered off, the number of the distribution network feeder line section closest to the power supply is selected and stored in a power-off set of a time period T;
if the distribution network feeder line sections from the distribution network feeder line main line to the branch line are not continuously powered off, the serial numbers of the distribution network feeder line sections which are powered off are stored into a power-off set in a T time period;
step S2: analyzing whether each numbered distribution network feeder line section in the power-off set of the T time period is a fault power-off or not in sequence;
intercepting r current discrete values Itij and r voltage discrete values Utij in a t time interval before power-off of a distribution network feeder line section with the number of i, wherein the t time interval can be a few microseconds, a few seconds, a few minutes and the like; i represents the number of the distribution network feeder line section, i=1, 2 … … i; j represents a time point in a t time interval, and may be Beijing time;
step S3: comparing r current discrete values Itij in the t time interval with normal current range values (Ii 1, ii 2), respectively; and comparing r voltage discrete values Utij in the t time interval with normal voltage range values (Ui 1, ui 2) respectively;
it can be understood that the normal current range value refers to the current range value that flows when the distribution network feeder line segment numbered i has no fault; similarly, the normal voltage range value refers to the terminal voltage range value of the distribution network feeder line section with the number i when no fault exists;
if r current discrete values Itij all belong to (Ii 1, ii 2) and r voltage discrete values Utij all belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is non-fault power-off;
if one of the r current discrete values Itij does not belong to (Ii 1, ii 2) or/and one of the r voltage discrete values Utij does not belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is indicated to be fault power-off; storing the serial numbers of the distribution network feeder line sections with fault outage in the time T into a fault outage set in the time T;
step S4: and the information classification module sends the fault outage set of all time periods in one year to the cloud platform module for storage.
In the application, the self-healing association module is used for analyzing the power information of the distribution network feeder line section and the corresponding environment information due to failure outage and establishing a mapping association table of environmental influence; the specific process is as follows:
step P1: extracting a fault outage set of a T time period from the cloud platform module, and extracting on-off state information of a corresponding distribution network feeder line section after the power is off in the T time period from the cloud platform module according to the serial numbers of the distribution network feeder line sections in the fault outage set of the T time period;
step P2: marking the distribution network feeder line sections which are electrified again after the power failure is carried out according to the distribution network feeder line fault self-healing technology in the T time period as self-healing electricity, and counting the number NT of the distribution network feeder line sections which are self-healing electricity in the T time period;
marking distribution network feeder line sections which are not powered off according to a distribution network feeder fault self-healing technology in a T time period as manual re-electrification, and counting the number MT of the distribution network feeder line sections which are manually re-electrified in the T time period;
according to the calculation formula: ηT=NT/(NT+MT), and obtaining a self-healing proportionality coefficient ηT of the distribution network feeder line in the T time period;
step P3: extracting environment information of self-healing electricity after fault outage in a period of T from a cloud platform module; sag HTk, galloping WTk, vibration ZTk, temperature DTk, relative air humidity STk, and lightning intensity QTk suffered, respectively; where k represents the number of detections at different time points in the T period, k=1, 2 … … k;
according to the calculation formula etk=a1×htk+a2× WTk +a3× ZTk +a4×dtk+a5×stk+a6× QTk; acquiring an environmental coefficient value ETk of a space where a distribution network feeder line at a k time point in a T time period is located, wherein a1, a2, a3, a4, a5 and a6 are preset proportion coefficient values;
step P4: arranging the environmental coefficient values ETk in the T time period in order from small to large, removing the maximum value and the minimum value, and forming the environmental coefficient range values (ET 1, ET 2) in the T time period by the remaining maximum environmental coefficient values and the remaining minimum environmental coefficient values;
step P5: mapping and correlating the self-healing proportionality coefficient eta T of the distribution network feeder line in the period of T with the environmental coefficient range values (ET 1 and ET 2); wherein T is a variable;
step P6: mapping and correlating the self-healing proportion coefficient of the distribution network feeder line and corresponding environment coefficient range values (ET 1, ET 2) in all time periods T in one year, establishing a mapping and correlating table of environmental influence, and sending the mapping and correlating table to the cloud platform module for storage.
In the application, the environment early warning module is used for early warning whether the current distribution network feeder can perform fault self-healing according to the mapping association table of the environment information and the environment influence of the current distribution network feeder detected in real time; the method is as follows:
the environment early warning module acquires real-time environment information of a current distribution network feeder from the cloud platform module and calculates an environment coefficient value;
comparing the calculated environment coefficient value with a mapping association table of the environment influence stored by the cloud platform module, and if the calculated environment coefficient value is in a corresponding environment coefficient range value, acquiring a self-healing proportionality coefficient;
if the current self-healing proportionality coefficient is larger than or equal to a preset self-healing proportionality coefficient threshold value, the environment early-warning module does not perform subsequent processing;
if the current self-healing proportionality coefficient is smaller than a preset self-healing proportionality coefficient threshold value, the environment early-warning module sends the position information of environment detection and an early-warning signal to the map construction module;
the self-healing proportional coefficient threshold value is preset and is obtained according to a large amount of experience;
the map construction module performs early warning labeling on the corresponding distribution network feeder line position of the electronic map according to the environment detection position information and the early warning signal, and uploads the information to the cloud platform module;
related staff can check early warning signals of self-healing of distribution network feeder faults in jurisdictions through the authorized cloud platform module, and corresponding preventive measures or treatment can be timely carried out.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the invention collects the power information, the environment information and the position information of the distribution network feeder line in the district through the information collection module; analyzing the power information of the distribution network feeder lines in the jurisdiction through an information classification module, so as to classify whether the power failure occurs to each distribution network feeder line section in the jurisdiction; analyzing the power information of the distribution network feeder line section due to failure and the corresponding environment information through a self-healing association module, and establishing a mapping association table of environmental influence; the environment early warning module is used for carrying out early warning on whether the current distribution network feeder can carry out fault self-healing according to the mapping association table of the environment information and the environment influence of the current distribution network feeder detected in real time, and the position information and the early warning signal of the environment detection are sent to the map construction module; the map construction module is used for constructing an electronic map according to the position information of the distribution network feeder line in the district, marking the acquired position information of the environment detection and the early warning signal on the electronic map, uploading the marked position information and the early warning signal to the cloud platform module, and authorizing a worker to log in the cloud platform module for preventive treatment.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented; the modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of this embodiment.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. The supervision and analysis system for the self-healing processing of the distribution network feeder line faults comprises a cloud platform module and is characterized by comprising: the system comprises an information acquisition module, an information classification module, a self-healing association module, an environment early warning module and a map construction module;
the information acquisition module is used for acquiring power information, environment information and position information of the distribution network feeder line in the district and uploading the power information, the environment information and the position information to the cloud platform module;
the information classification module is used for analyzing the power information of the distribution network feeder lines in the jurisdiction so as to classify whether the power failure occurs to each distribution network feeder line section in the jurisdiction or not and upload the power failure to the cloud platform module;
the self-healing association module is used for analyzing the power information of the distribution network feeder line section and the corresponding environment information due to failure outage, establishing a mapping association table of the environment influence and uploading the mapping association table to the cloud platform module; comprising the following steps:
step P1: extracting a fault outage set of a T time period from the cloud platform module, and extracting on-off state information of a corresponding distribution network feeder line section after the power is off in the T time period from the cloud platform module according to the serial numbers of the distribution network feeder line sections in the fault outage set of the T time period;
step P2: marking the distribution network feeder line sections which are electrified again after the power failure is carried out according to the distribution network feeder line fault self-healing technology in the T time period as self-healing electricity, and counting the number NT of the distribution network feeder line sections which are self-healing electricity in the T time period; marking distribution network feeder line sections which are not powered off according to a distribution network feeder fault self-healing technology in a T time period as manual re-electrification, and counting the number MT of the distribution network feeder line sections which are manually re-electrified in the T time period;
according to the calculation formula: ηT=NT/(NT+MT), and obtaining a self-healing proportionality coefficient ηT of the distribution network feeder line in the T time period;
step P3: extracting self-healing electricity environment information after fault outage in a period of T from a cloud platform module, wherein the environment information is sag HTk, galloping WTk, vibration ZTk, temperature DTk, relative air humidity STk and lightning intensity QTk; where k represents the number of detections at different time points in the T period, k=1, 2 … … k;
according to the calculation formula etk=a1×htk+a2× WTk +a3× ZTk +a4×dtk+a5×stk+a6× QTk; acquiring an environmental coefficient value ETk of a space where a distribution network feeder line at a k time point in a T time period is located, wherein a1, a2, a3, a4, a5 and a6 are preset proportion coefficient values;
step P4: arranging the environmental coefficient values ETk in the T time period in order from small to large, removing the maximum value and the minimum value, and forming the environmental coefficient range values (ET 1, ET 2) in the T time period by the remaining maximum environmental coefficient values and the remaining minimum environmental coefficient values;
step P5: mapping and correlating the self-healing proportionality coefficient eta T of the distribution network feeder line in the period of T with the environmental coefficient range values (ET 1 and ET 2); wherein T is a variable;
step P6: mapping and correlating the self-healing proportion coefficient of the distribution network feeder line and corresponding environment coefficient range values (ET 1, ET 2) in all time periods T in one year of history, establishing a mapping and correlating table of environmental influence, and sending the mapping and correlating table to a cloud platform module for storage;
the environment early warning module is used for carrying out early warning on whether the current distribution network feeder can carry out fault self-healing according to the mapping association table of the environment information and the environment influence of the current distribution network feeder detected in real time, and sending the position information and the early warning signal of the environment detection to the map construction module;
the map construction module is used for constructing an electronic map according to the position information of distribution network feeder lines in the jurisdiction, marking the acquired position information and early warning signals of environment detection on the electronic map, uploading the position information and the early warning signals to the cloud platform module, and preventing and processing by staff.
2. The supervision and analysis system for distribution network feeder fault self-healing processing according to claim 1, wherein the power information of the distribution network feeder in the district comprises current, voltage and on-off state of each distribution network feeder line section; the environment information of the distribution network feeder lines in the jurisdiction comprises the arc sagging, the galloping, the vibration, the temperature and the relative air humidity of the environment where each distribution network feeder line section is located and the lightning intensity suffered.
3. The supervision and analysis system for distribution network feeder fault self-healing processing according to claim 2, wherein the process of analyzing the power information of the distribution network feeder in the jurisdiction by the information classification module is as follows:
step S1: extracting power information in a history year from the cloud platform module, and numbering each distribution network feeder line section according to the primary and secondary orders of the distribution network feeder; screening the distribution network feeder line sections which are not powered off and not powered back in the same time period T, and storing the numbers of the distribution network feeder line sections into a power-off set in the time period T;
step S2: analyzing whether each numbered distribution network feeder line section in the power-off set of the T time period is a fault power-off or not in sequence;
intercepting r current discrete values Itij and r voltage discrete values Utij in a t time interval before power-off of a distribution network feeder line section with the number of i; i represents the number of the distribution network feeder line section, i=1, 2 … … i; j represents a time point within a t time interval;
step S3: comparing r current discrete values Itij in the t time interval with normal current range values (Ii 1, ii 2), respectively; and comparing r voltage discrete values Utij in the t time interval with normal voltage range values (Ui 1, ui 2) respectively;
if r current discrete values Itij all belong to (Ii 1, ii 2) and r voltage discrete values Utij all belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is non-fault power-off;
if one of the r current discrete values Itij does not belong to (Ii 1, ii 2) or/and one of the r voltage discrete values Utij does not belong to (Ui 1, ui 2), the power-off state of the distribution network feeder line section with the number i in the time period T is indicated to be fault power-off; storing the serial numbers of the distribution network feeder line sections with fault outage in the time T into a fault outage set in the time T;
step S4: and the information classification module sends the fault outage set of all time periods in one year to the cloud platform module for storage.
4. The supervision and analysis system for distribution network feeder line fault self-healing processing according to claim 3, wherein in step S1, if the distribution network feeder line sections from the main line to the branch line of the distribution network feeder line are continuously powered off, the number of the distribution network feeder line section closest to the power supply is selected and stored in the power-off set of the T time period; if the distribution network feeder line sections from the distribution network feeder line main line to the branch line are not continuously powered off, the numbers of the distribution network feeder line sections which are powered off are stored into a power-off set in the T time period.
5. A supervision and analysis system for distribution network feeder fault self-healing processing according to claim 3, wherein the normal current range value (Ii 1, ii 2) refers to the current range value that flows when the distribution network feeder line segment numbered i has no fault; the normal voltage range values (Ui 1, ui 2) refer to the end voltage range values of the distribution network feeder line section numbered i when there is no fault.
6. The supervision and analysis system for self-healing processing of distribution network feeder line faults according to claim 1, wherein the early warning mode of the environment early warning module is as follows:
acquiring real-time environment information of a current distribution network feeder from a cloud platform module, and calculating an environment coefficient value;
comparing the calculated environment coefficient value with a mapping association table of the environment influence stored by the cloud platform module, and if the calculated environment coefficient value is in a corresponding environment coefficient range value, acquiring a self-healing proportionality coefficient;
if the current self-healing proportionality coefficient is larger than or equal to a preset self-healing proportionality coefficient threshold value, the environment early-warning module does not perform subsequent processing;
if the current self-healing proportionality coefficient is smaller than the preset self-healing proportionality coefficient threshold value, the environment early-warning module sends the position information of the environment detection and the early-warning signal to the map construction module.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006053728A (en) * | 2004-08-11 | 2006-02-23 | Nec Corp | Failure handling rule propagation method, failure restoration device and program |
| CN102231521A (en) * | 2011-06-24 | 2011-11-02 | 中国电力科学研究院 | Power grid operation state identification method in distribution network self-healing control |
| CN115796577A (en) * | 2022-11-21 | 2023-03-14 | 嘉兴恒创电力集团有限公司博创物资分公司 | Lightning risk early warning method for distribution network |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7184903B1 (en) * | 2006-03-16 | 2007-02-27 | Vrb Power Systems Inc. | System and method for a self-healing grid using demand side management techniques and energy storage |
| US8504214B2 (en) * | 2010-06-18 | 2013-08-06 | General Electric Company | Self-healing power grid and method thereof |
-
2023
- 2023-04-21 CN CN202310430676.3A patent/CN116436162B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006053728A (en) * | 2004-08-11 | 2006-02-23 | Nec Corp | Failure handling rule propagation method, failure restoration device and program |
| CN102231521A (en) * | 2011-06-24 | 2011-11-02 | 中国电力科学研究院 | Power grid operation state identification method in distribution network self-healing control |
| CN115796577A (en) * | 2022-11-21 | 2023-03-14 | 嘉兴恒创电力集团有限公司博创物资分公司 | Lightning risk early warning method for distribution network |
Non-Patent Citations (5)
| Title |
|---|
| Liu Haike ; Yubao Ren.Research on Applicatin and Challenges of Large Data in Smart Grid.DEStech Transactions on Environment, Energy and Earth Sciences.2019,第1-6页. * |
| Rajan Kumar Mishra ; K. Shanti Swarup.Restoration of smart distribution grid using self healing characteristics in multi agent environment.2015 Annual IEEE India Conference (INDICON).2016,第1-6页. * |
| 含DG的智能配电网快速自愈技术研究;秦立军, 张国彦, 陈晓东;电测与仪表;第58卷(第07期);第67-73页 * |
| 智能配电网自愈控制及其关键技术研究;刘秋华;董丹丹;孟珊珊;吴成立;;南京工程学院学报(自然科学版);第13卷(第03期);第31-36页 * |
| 智能配电网调度控制系统核心指标的构建;姜宁, 张磐, 汪宇倩;电力系统及其自动化学报;第30卷(第05期);第86-91页 * |
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