CN116559591A - Intelligent power transmission and distribution distributed fault diagnosis and type identification system - Google Patents
Intelligent power transmission and distribution distributed fault diagnosis and type identification system Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/52—Testing for short-circuits, leakage current or ground faults
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Abstract
An intelligent power transmission and distribution distributed fault diagnosis and type identification system comprises a power transmission and distribution line monitoring device and a fault management system, wherein the power transmission and distribution line monitoring device comprises a power frequency and traveling wave acquisition module, a GPS clock, a signal processing module, an anti-interference module, a signal transmission module and a communication module; the fault management system comprises a data center, fault diagnosis, fault type identification and fault report generation; the intelligent power transmission and distribution distributed fault diagnosis and type identification system can rapidly acquire fault information and accurately diagnose and identify faults under the conditions of multiple ends of power supply modes, complex structure and multiple branches through the improved traveling wave method and the power transmission and distribution line monitoring device, and the applicable power transmission and distribution lines not only comprise overhead lines but also comprise cables.
Description
Technical Field
The invention relates to the technical field of intelligent fault diagnosis, in particular to an intelligent power transmission and distribution distributed fault diagnosis and type identification system.
Background
The rapid and stable development of the power industry promotes the economic construction of society, can provide sufficient power for people to develop and develop, and meets the daily life demands of various fields and people. However, the continuous increase of electric equipment brings huge pressure to the power transmission and distribution lines, and the power transmission and distribution lines are easily subjected to fault problems due to the influence of factors such as operating environment, climate conditions and the like. The fault of the power transmission and distribution line not only can influence the supply of the power, but also can cause certain damage to some power transmission related equipment, and the operation cost of power enterprises and the inconvenience of people living can be greatly increased. Therefore, when the fault occurs, fault diagnosis is completed in the shortest time and the fault is accurately positioned, so that the normal operation of the power system is ensured.
At present, fault diagnosis and classification in power transmission and distribution lines mainly comprise an impedance method and a traveling wave method. The principle of the impedance method is to calculate the impedance of a fault loop according to the voltage and the current measured during fault, and the position of a fault point can be obtained because the length of a line is in direct proportion to the impedance, but the impedance method is easily influenced by variable factors such as excess resistance, line distributed capacitance, line model, operation mode and the like, so the measurement accuracy is not high. The traveling wave method is more practical in power transmission and distribution lines, and utilizes transient traveling wave analysis generated by fault points to obtain fault related information, and in principle, the measurement accuracy, the line length and the structure of the traveling wave method can achieve high-accuracy fault diagnosis, but the traveling wave method also needs to solve the new problem of a modern power supply system: firstly, the power grid is in a multi-terminal power supply mode, the structure is complex, the branches are more, and certain difficulty exists in fault partition; secondly, the speed of the electricity in the power line and the speed of the electricity in the cable are different, and the algorithm difficulty is increased when fault positioning is carried out; thirdly, the wave speed of the traveling wave method is easily influenced by weather environment and the like, so that the wave speed is unstable, and the measurement accuracy of the traveling wave method is reduced due to factors such as time asynchronism caused by time delay of a detection device, so that a new system based on the traveling wave method is required to be researched for fault diagnosis and type identification of power transmission and distribution lines.
Disclosure of Invention
The invention aims to solve the defects of fault identification diagnosis and type identification of a power transmission and distribution line by adopting a traveling wave method, and provides an intelligent power transmission and distribution distributed fault diagnosis and type identification system.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent power transmission and distribution distributed fault diagnosis and type identification system, comprising: a power transmission and distribution line monitoring device and a fault management system; the power transmission and distribution line monitoring device comprises:
the power frequency and traveling wave acquisition module is used for acquiring line power frequency current, power frequency voltage and fault traveling wave information data;
the GPS clock is used for generating high-precision synchronous clock data and calculating the position of a fault point, so that each power transmission and distribution line monitoring device has no time difference, and the precision of fault electricity is further greatly improved;
the signal processing module is used for carrying out steepening on the collected abnormal discharge or fault traveling wave signals and fault power frequency signals, and the steepened traveling wave is easy to detect, high in precision and high in reliability;
the anti-interference module is used for completely shielding and isolating parts sensitive to interference signals (such as a power supply, an acquisition channel and the like) and ensuring the accuracy of data acquired by the power frequency and traveling wave acquisition module;
the signal transmission module adopts double-shielding radio frequency materials, and the traveling wave signals are transmitted in parallel by double symmetry, so that the anti-interference capability and the reliability in the transmission process are improved;
the communication module is used for establishing a data communication channel with the fault management system and sending the data of the acquisition mark to the fault management system accurately in real time;
the fault management system includes:
the data center is used for receiving the data uploaded by the power transmission and distribution line monitoring device and issuing related control instructions;
the fault diagnosis is used for analyzing the fault information, calculating the fault position and completing fault early warning;
identifying fault types, namely identifying lightning wave and common line short-circuit faults according to traveling wave characteristic frequency spectrum information, and identifying shielding failure and counterattack failure;
generating a fault report, accurately recording the number and the position of lightning strokes of a line, accumulating lightning stroke characteristic parameters, and generating a line maintenance opinion report.
As a preferred technical solution, the fault diagnosis includes the following steps:
s1: extracting traveling wave data of the data center, and converting vector data of the traveling wave into a modulus form through a phasor-mode conversion relation;
s2: performing reverse steepening treatment on the traveling wave data, and recovering the original waveform of the traveling wave signal;
s3: calculating instantaneous energy of the decomposed high-frequency components, and determining a traveling wave head through peak moment in the instantaneous energy;
s4: establishing a fault branch search matrix by utilizing the multi-end traveling wave time difference and the double-end traveling wave principle, and determining a fault branch line through matrix element change;
s5: positioning a fault point;
s6: performing fault early warning;
as a preferred solution, the modulus form into which the travelling wave vector data is converted contains an α -mode and a β -mode, both modes being used alone or in combination to diagnose any type of fault.
As a preferable technical scheme, the specific steps of the step S3 are as follows:
step S31, carrying out band-pass filtering on the original traveling wave signal to remove low-frequency interference components and keep high-frequency components;
step S32: decomposing a target signal by using an HU-CEEMDAN algorithm to obtain a plurality of IMF components, respectively calculating correlation coefficients and permutation entropy values of the components, and constructing a correlation permutation entropy function to obtain a signal capable of best characterizing fault characteristics;
s33, processing the single-component signal of the fault moving waveform after the improved signal decomposition by using TEO to detect the change of energy of frequency components, and further capturing a fault traveling wave head;
the instantaneous amplitude and instantaneous frequency of the instantaneous energy signal are obtained as follows:
wherein a (t) is a time-varying amplitude;is a time-varying phase; θ is the initial phase; q (τ) is a normalized Frequency Modulated (FM) signal; omega c Is the carrier frequency; omega m Is the maximum frequency deviation;
the energy operator of the single component signal is given in the following equation;
ψ[s′(t)]＝a 2 (t)ω 4 (t)
from the above equation, the instantaneous amplitude of the signal s (t) can be obtained as follows:
the instantaneous frequency of the signal s (t) is as follows:
as a preferable technical solution, the step S4 specifically includes the following steps:
step S40: dividing maintenance areas according to a minimum maintenance unit in the power system, and creating a traveling wave abnormal signal monitor in each maintenance area;
step S41: when the monitor receives the abnormal information, constructing a fault branch search matrix H of (n-1) x (n-1), wherein n is the node number of the maintenance area;
step S42: for each pair of nodes i and j, calculating a propagation time difference delta tij between the nodes i and j by using a double-end traveling wave principle, wherein i is the node where the fault point is located;
step S43: for each non-fault node k, calculating the time ti, k required by the node to the fault point by utilizing the multi-terminal traveling wave time difference principle;
step S44: from step S42 and step S43 we can calculate the time difference between node i and node k, Δ tik, i.e. Δ tik =ti, k-ti, i+Δtij;
step S45: if the value of Δ tik is less than 0, then it is assumed that node k is on the other side of the fault point, at which time the ith row and kth column of H are set to-1; if the value of Δ tik is greater than 0, it is indicated that node k is on the same side of the failure point, at which time the ith row and kth column of H are set to 1; if the value of Δ tik is equal to 0, it indicates that node k is located at the failure point, at which time the ith row and kth column of H are set to 0;
step S46: repeating the steps 42 to 45, and processing each pair of nodes i and j to finally obtain a fault branch search matrix H;
step S47: determining a power transmission line fault branch according to the change characteristics of the fault branch search matrix H;
step S48: calculate the sum of the column vectors of H, i.e., si= Σjh (j, i), i=1, 2,..;
if si=0, it is stated that node i is on the faulty branch, which can then be determined directly,
if Si= + -1, it is indicated that the node i is located at one side of the faulty branch, searching needs to be continued, and a node of Si= + -1 is selected as a new starting point, and searching is continued until the faulty branch is found;
as a preferred technical solution, the intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 1 is characterized in that the step S5 specifically comprises the following steps:
step S51: starting a line at a fault point to form a double-end branch end point;
step S52: calculating initial distances from the fault point to other nodes of the double-ended branch to obtain a fault distance D= [ D1, D2, ], dn ], wherein di represents the fault distance of the double-ended branch from the initial end to the node i;
wherein t is S1 ，t S2 ，t E1 ，t E2 The wave head time of the alpha and beta mode components reaching the S end and the i end of the starting end respectively;
step S53, according to the fault distance D, a corresponding weight vector W= [ W1, W2, ], wherein the weight wi is determined according to factors such as the length of the double-ended branch, transmission line parameters and the like;
step S54: according to the weight vector W, calculating a weighted average value to obtain a final fault distance d 0 ，
Where d= [ D1, D2, ], dn ] is a fault distance vector, w= [ W1, W2, ], wn ] is a weight vector, and n represents the number of nodes.
In summary, the intelligent power transmission and distribution distributed fault diagnosis and type identification system has the following advantages: the anti-interference design is adopted, the accuracy of traveling wave data acquisition is ensured, the traveling wave transmission adopts a double-shielding radio frequency design, the influence of external environment in the transmission process is avoided, the steepening treatment of the traveling wave signals enables the data to be more easily identified, and the traveling wave data is ensured not to be lost in any precision and accuracy in the whole process of generation, transportation and reception.
In fault diagnosis of the fault management system, the HU-CEEMDAN traveling wave detection algorithm is used, the problem of characteristic frequency aliasing is avoided, the wave head capturing precision is improved, the line fault distance is calculated by using alpha and beta modulus components of modulus, the influence of wave speed on positioning results caused by different overhead lines and cables can be eliminated, a fault search matrix is established, a search fault matrix is established according to the minimum maintenance unit division maintenance area in the power system, and accurate selection and positioning of fault branches are realized.
Drawings
FIG. 1 is a schematic diagram of an intelligent monitoring system for setting electric power and traffic foundation according to the present invention;
FIG. 2 is a schematic diagram of a fault diagnosis device for power transmission and distribution lines of an intelligent monitoring system for setting electric power and traffic foundation according to the present invention;
FIG. 3 is a schematic diagram of functional blocks of a fault diagnosis module shown in a schematic diagram of a power and traffic foundation arrangement intelligent monitoring system of FIG. 1;
Detailed Description
For a better understanding of the objects, structures and functions of the present invention, reference should be made to the following detailed description of the invention, taken in conjunction with the accompanying drawings and examples, which are meant to illustrate, not to limit, the invention.
Referring to fig. 1, the power transmission and distribution line monitoring device and the fault management system of the present invention include: power transmission and distribution line monitoring devices and fault management system, power transmission and distribution line monitoring devices includes:
the power frequency and traveling wave acquisition module is used for automatically identifying and acquiring abnormal discharge, fault traveling wave signals and fault power frequency signals of the power transmission and distribution line;
the GPS clock is used for generating high-precision synchronous clock data and calculating the position of the fault point;
the signal processing module is used for carrying out steepening processing on the collected abnormal discharge or fault traveling wave signals and fault power frequency signals;
the anti-interference module is used for shielding and isolating a power supply, an acquisition channel and the like which are sensitive to interference travelling wave signals;
the signal transmission module adopts double-shielding radio frequency materials and transmits traveling wave signals in parallel in a double-root symmetrical manner;
the communication module is used for establishing a data communication channel with the fault management system and sending the data of the acquisition mark to the fault management system accurately in real time;
the fault management system includes:
the data center is used for receiving the data uploaded by the power transmission and distribution line monitoring device and issuing related control instructions;
the fault diagnosis is used for analyzing the fault information, calculating the fault position and completing fault early warning;
identifying fault types, namely identifying lightning wave and common line short-circuit faults according to traveling wave characteristic frequency spectrum information, and identifying shielding failure and counterattack failure;
generating a fault report, accurately recording the number and the position of lightning strokes of a line, accumulating lightning stroke characteristic parameters, and generating a line maintenance opinion report.
The invention relates to a power transmission and distribution line monitoring device and a fault management system, which have the following working principles: the power transmission and distribution line monitoring device is arranged on a power transmission and distribution line guide line, one power transmission and distribution line guide line is arranged on each phase, one power transmission and distribution line monitoring device comprises three devices, and when the power transmission and distribution line works normally, the power transmission and distribution line monitoring device measures line current and uploads a current effective value to the fault management system. When abnormal discharge or faults occur in the power transmission and distribution line, current traveling waves are transmitted to two ends along the line, a hardware traveling wave channel of the power transmission and distribution line monitoring device is used for collecting wave heads of the current traveling waves, simultaneously, high-frequency traveling wave signal wave recording is started, if a circuit breaker of the line trips, power frequency current wave recording is started, accurate time is marked, the power transmission and distribution line monitoring device uploads traveling waves and wave recording files to a fault management system, the fault management system establishes a fault search matrix according to traveling wave information of each monitoring point, accurate selection and fault of fault branches are achieved, abnormal discharge early warning or accurate fault positioning is achieved by adopting a network positioning method based on local double-end positioning, and fault type identification is achieved by utilizing traveling wave recording file traveling wave spectrum information.
The fault diagnosis comprises the following steps:
s1: extracting traveling wave data of the data center, and converting vector data of the traveling wave into a modulus form through a phasor-mode conversion relation;
s2: performing reverse steepening treatment on the traveling wave data, and recovering the original waveform of the traveling wave signal;
s3: calculating instantaneous energy of the decomposed high-frequency components, and determining a traveling wave head through peak moment in the instantaneous energy;
s4: establishing a fault branch search matrix by utilizing the multi-end traveling wave time difference and the double-end traveling wave principle, and determining a fault branch line through matrix element change;
s5: positioning a fault point;
s6: performing fault early warning;
the specific steps of the step S3 are as follows:
step S31, carrying out band-pass filtering on the original traveling wave signal to remove low-frequency interference components and keep high-frequency components;
step S32: decomposing a target signal by using an HU-CEEMDAN algorithm to obtain a plurality of IMF components, respectively calculating correlation coefficients and permutation entropy values of the components, and constructing a correlation permutation entropy function to obtain a signal capable of best characterizing fault characteristics;
s33, processing the single-component signal of the fault moving waveform after the improved signal decomposition by using TEO to detect the change of energy of frequency components, and further capturing a fault traveling wave head;
the instantaneous amplitude and instantaneous frequency of the instantaneous energy signal are obtained as follows:
wherein a (t) is a time-varying amplitude;is a time-varying phase; θ is the initial phase; q (τ) is a normalized Frequency Modulated (FM) signal; omega c Is the carrier frequency; omega m Is the maximum frequency deviation;
the energy operator of the single component signal is given in the following equation;
ψ[s′(t)]＝a 2 (t)ω 4 (t)
from the above equation, the instantaneous amplitude of the signal s (t) can be obtained as follows:
the instantaneous frequency of the signal s (t) is as follows:
the specific steps of the step S4 are as follows:
step S40: dividing maintenance areas according to a minimum maintenance unit in the power system, and creating a traveling wave abnormal signal monitor in each maintenance area;
step S41: when the monitor receives the abnormal information, constructing a fault branch search matrix H of (n-1) x (n-1), wherein n is the node number of the maintenance area;
step S42: for each pair of nodes i and j, calculating a propagation time difference delta tij between the nodes i and j by using a double-end traveling wave principle, wherein i is the node where the fault point is located;
step S43: for each non-fault node k, calculating the time ti, k required by the node to the fault point by utilizing the multi-terminal traveling wave time difference principle;
step S44: from step S42 and step S43 we can calculate the time difference between node i and node k, Δ tik, i.e. Δ tik =ti, k-ti, i+Δtij;
step S45: if the value of Δ tik is less than 0, then it is assumed that node k is on the other side of the fault point, at which time the ith row and kth column of H are set to-1; if the value of Δ tik is greater than 0, it is indicated that node k is on the same side of the failure point, at which time the ith row and kth column of H are set to 1; if the value of Δ tik is equal to 0, it indicates that node k is located at the failure point, at which time the ith row and kth column of H are set to 0;
step S46: repeating the steps 42 to 45, and processing each pair of nodes i and j to finally obtain a fault branch search matrix H;
step S47: determining a power transmission line fault branch according to the change characteristics of the fault branch search matrix H;
step S48: calculate the sum of the column vectors of H, i.e., si= Σjh (j, i), i=1, 2,..;
if si=0, it is stated that node i is on the faulty branch, which can then be determined directly,
if Si= + -1, it is indicated that the node i is located at one side of the faulty branch, searching needs to be continued, and a node of Si= + -1 is selected as a new starting point, and searching is continued until the faulty branch is found;
the specific steps of the step S5 are as follows:
step S51: starting a line at a fault point to form a double-end branch end point;
step S52: calculating initial distances from the fault point to other nodes of the double-ended branch to obtain a fault distance D= [ D1, D2, ], dn ], wherein di represents the fault distance of the double-ended branch from the initial end to the node i;
wherein t is S1 ，t S2 ，t E1 ，t E2 The wave head time of the alpha and beta mode components reaching the s end and the i end of the starting end respectively;
step S53, according to the fault distance D, a corresponding weight vector W= [ W1, W2, ], wherein the weight wi is determined according to factors such as the length of the double-ended branch, transmission line parameters and the like;
step S54: according to the weight vector W, calculating a weighted average value to obtain a final fault distance d 0 ，
Where d= [ D1, D2, ], dn ] is a fault distance vector, w= [ W1, W2, ], wn ] is a weight vector, and n represents the number of nodes.
It will be understood that the invention has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (6)
1. An intelligent power transmission and distribution distributed fault diagnosis and type identification system, comprising: power transmission and distribution line monitoring devices and fault management system, power transmission and distribution line monitoring devices includes:
the power frequency and traveling wave acquisition module is used for automatically identifying and acquiring abnormal discharge, fault traveling wave signals and fault power frequency signals of the power transmission and distribution line;
the GPS clock is used for generating high-precision synchronous clock data and calculating the position of the fault point;
the signal processing module is used for carrying out steepening processing on the collected abnormal discharge or fault traveling wave signals and fault power frequency signals;
the anti-interference module is used for shielding and isolating a power supply, an acquisition channel and the like which are sensitive to interference travelling wave signals;
the signal transmission module adopts double-shielding radio frequency materials and transmits traveling wave signals in parallel in a double-root symmetrical manner;
the communication module is used for establishing a data communication channel with the fault management system and sending the data of the acquisition mark to the fault management system accurately in real time;
the fault management system includes:
the data center is used for receiving the data uploaded by the power transmission and distribution line monitoring device and issuing related control instructions;
the fault diagnosis is used for analyzing the fault information, calculating the fault position and completing fault early warning;
identifying fault types, namely identifying lightning wave and common line short-circuit faults according to traveling wave characteristic frequency spectrum information, and identifying shielding failure and counterattack failure;
generating a fault report, accurately recording the number and the position of lightning strokes of a line, accumulating lightning stroke characteristic parameters, and generating a line maintenance opinion report.
2. An intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 1, characterized in that said fault diagnosis comprises the steps of:
s1: extracting traveling wave data of the data center, and converting vector data of the traveling wave into a modulus form through a phasor-mode conversion relation;
s2: performing reverse steepening treatment on the traveling wave data, and recovering the original waveform of the traveling wave signal;
s3: calculating instantaneous energy of the decomposed high-frequency components, and determining a traveling wave head through peak moment in the instantaneous energy;
s4: establishing a fault branch search matrix by utilizing the multi-end traveling wave time difference and the double-end traveling wave principle, and determining a fault branch line through matrix element change;
s5: positioning a fault point;
s6: and (5) fault early warning.
3. The intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 2, wherein the modulus form converted from the traveling wave vector data comprises an alpha module and a beta module, and both modes can be used independently for diagnosing any type of fault or can be used in combination.
4. The intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 1, wherein the specific steps of step S3 are as follows:
step S31, carrying out band-pass filtering on the original traveling wave signal to remove low-frequency interference components and keep high-frequency components;
step S32: decomposing a target signal by using an HU-CEEMDAN algorithm to obtain a plurality of IMF components, respectively calculating correlation coefficients and permutation entropy values of the components, and constructing a correlation permutation entropy function to obtain a signal capable of best characterizing fault characteristics;
s33, processing the single-component signal of the fault moving waveform after the improved signal decomposition by using TEO to detect the change of energy of frequency components, and further capturing a fault traveling wave head;
the instantaneous amplitude and instantaneous frequency of the instantaneous energy signal are obtained as follows:
wherein a (t) is a time-varying amplitude;is a time-varying phase; θ is the initial phase; q (τ) is a normalized Frequency Modulated (FM) signal; omega c Is the carrier frequency; omega m Is the maximum frequency deviation;
the energy operator of the single component signal is given in the following equation;
ψ[s′(t)]＝a 2 (t)ω 4 (t)
from the above equation, the instantaneous amplitude of the signal s (t) can be obtained as follows:
the instantaneous frequency of the signal s (t) is as follows:
5. the intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 1, wherein the specific steps of step S4 are as follows:
step S40: dividing maintenance areas according to a minimum maintenance unit in the power system, and creating a traveling wave abnormal signal monitor in each maintenance area;
step S41: when the monitor receives the abnormal information, constructing a fault branch search matrix H of (n-1) x (n-1), wherein n is the node number of the maintenance area;
step S42: for each pair of nodes i and j, calculating a propagation time difference delta tij between the nodes i and j by using a double-end traveling wave principle, wherein i is the node where the fault point is located;
step S43: for each non-fault node k, calculating the time ti, k required by the node to the fault point by utilizing the multi-terminal traveling wave time difference principle;
step S44: from step S42 and step S43 we can calculate the time difference between node i and node k, Δ tik, i.e. Δ tik =ti, k-ti, i+Δtij;
step S45: if the value of Δ tik is less than 0, then it is assumed that node k is on the other side of the fault point, at which time the ith row and kth column of H are set to-1; if the value of Δ tik is greater than 0, it is indicated that node k is on the same side of the failure point, at which time the ith row and kth column of H are set to 1; if the value of Δ tik is equal to 0, it indicates that node k is located at the failure point, at which time the ith row and kth column of H are set to 0;
step S46: repeating the steps 42 to 45, and processing each pair of nodes i and j to finally obtain a fault branch search matrix H;
step S47: determining a power transmission line fault branch according to the change characteristics of the fault branch search matrix H;
step S48: the sum of the column vectors of H, i.e. Si = Σ j H (j, i),
i＝1,2,...,n-1；
if si=0, it is stated that node i is on the faulty branch, which can then be determined directly,
if si= ±1, it means that the node i is located at one side of the faulty branch, the search needs to be continued, and a node si= ±1 is selected as a new starting point, and the search is continued until the faulty branch is found.
6. The intelligent power transmission and distribution distributed fault diagnosis and type identification system according to claim 1, wherein the specific steps of step S5 are as follows:
step S51: starting a line at a fault point to form a double-end branch end point;
step S52: calculating initial distances from the fault point to other nodes of the double-ended branch to obtain a fault distance D= [ D1, D2, ], dn ], wherein di represents the fault distance of the double-ended branch from the initial end to the node i;
wherein t is S1 ，t S2 ，t E1 ，t E2 The wave head time of the alpha and beta mode components reaching the starting end and the i end respectively;
step S53, according to the fault distance D, a corresponding weight vector W= [ W1, W2, ], wherein the weight wi is determined according to factors such as the length of the double-ended branch, transmission line parameters and the like;
step S54: according to the weight vector W, calculating a weighted average value to obtain a final fault distance d 0 ，
Where d= [ D1, D2, ], dn ] is a fault distance vector, w= [ W1, W2, ], wn ] is a weight vector, and n represents the number of nodes.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117148044A (en) * | 2023-09-19 | 2023-12-01 | 山东华科信息技术有限公司 | Power distribution network fault positioning method and device based on artificial intelligence |
| CN117572157A (en) * | 2024-01-15 | 2024-02-20 | 湖南湘能智能电器股份有限公司 | Distribution network line abnormal traveling wave positioning method and system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117148044A (en) * | 2023-09-19 | 2023-12-01 | 山东华科信息技术有限公司 | Power distribution network fault positioning method and device based on artificial intelligence |
| CN117148044B (en) * | 2023-09-19 | 2024-04-02 | 山东华科信息技术有限公司 | Power distribution network fault positioning method and device based on artificial intelligence |
| CN117572157A (en) * | 2024-01-15 | 2024-02-20 | 湖南湘能智能电器股份有限公司 | Distribution network line abnormal traveling wave positioning method and system |
| CN117572157B (en) * | 2024-01-15 | 2024-04-12 | 湖南湘能智能电器股份有限公司 | Distribution network line abnormal traveling wave positioning method and system |
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