A method for locating interference sources in distribution network based on monitoring point configuration optimization

Abstract

The present invention relates to a method for locating the position of a distribution network interference source based on monitoring point configuration optimization, comprising the following steps: collecting power quality data of each power quality monitor PQM, the power quality monitor being installed at a number of system nodes based on a preset monitoring point configuration optimization principle, the power quality data including the result of interference direction judgment; estimating the power quality data of all system nodes within the whole network detection target based on known line data and the collected power quality data; using a genetic algorithm to solve the power quality data of all system nodes to obtain an optimal solution, and obtaining the interference source position according to the optimal solution. Compared with the prior art, the present invention uses the least PQM to achieve the best monitoring effect on the system without reducing the accuracy of locating the power quality interference source, can quickly and accurately determine the position of the power quality interference source, improve the power supply reliability of the distribution network, and has strong practical significance and application value.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and relates to a method for locating interference sources in a distribution network with distributed power sources, and in particular to a method for locating the position of interference sources in a distribution network based on optimization of monitoring point configuration. Background Art

[0002] The distribution network is an important bridge connecting the end-users of electricity and the power system, and is an indispensable part of the entire power supply and distribution system. In the distribution network, overhead lines and cable lines are used in a mixed manner, the network structure is complex, and there are many branch points. According to statistics, most of the power outages suffered by power users are caused by distribution network failures. When a distribution network fails, the quality of electricity used by customers will be seriously reduced. If the monitoring system of the distribution network fails to detect and eliminate the fault in time, it will cause damage to personnel or equipment. In severe cases, it will also lead to the expansion of the power outage area, the inability of the system to operate safely and stably, and other inestimable losses.

[0003] In recent years, distributed power sources have developed rapidly. These new power sources have made great contributions to alleviating power shortages and improving energy structure, but at the same time, their connection to the grid has also brought new problems and challenges to the optimization and control of traditional power systems. After the distributed power sources are connected to the distribution system, the grid structure and operation control mode will undergo tremendous changes. The grid will change from a single power supply mode to a multi-power supply mode. The location, capacity and operation mode of the distributed power sources will have a great impact on the line flow, node voltage and network loss of the grid. The emergence of distributed power sources has made the power quality problem more serious on the one hand, and on the other hand, its uncertain flow direction has seriously affected the determination of the interference source direction, making it more difficult to locate the power quality interference source.

[0004] Only by accurately locating the power quality interference source can effective measures be taken to eliminate or reduce the impact of the disturbance. For both the power supply and the user, the power quality interference source location can also clarify the responsibilities of both parties and determine whether the disturbance is caused by the power system or by the user, which is an important basis for coordinating disputes between the power department and the user in the power market environment. The power quality interference source location also plays an important guiding role in the power department's formulation of power quality control strategies.

[0005] Nowadays, the monitoring system generally used in distribution networks is an automated system that can communicate bidirectionally between terminals, substations and master stations. There are a large number of distributed power sources DG (Distribute Generation, DG) in the distribution network, including wind energy, solar energy, etc. However, the extensive use of DG has changed the distribution network from a traditional single-power radiation network to a multi-power network with a more complex and changeable structure, and the power direction in the network has also changed from unidirectional to bidirectional. This puts forward new requirements for the data acquisition and transmission and anti-interference capabilities of the feeder terminal unit FTU (Feeder Terminal Unit, FTU). With the complex evolution of the distribution network structure, the amount of information reported by FTU is becoming larger and larger, and the traditional fault section location algorithm suitable for the radiation network needs to be improved accordingly. Therefore, it is very necessary for power grid companies, power grid users and the popularization and development of DG to study a method that can quickly extract key information and accurately locate the fault area after a distribution network fault occurs.

[0006] The power quality monitoring point network is a solution that can quickly extract key information and accurately locate the fault area after a distribution network fault occurs. However, the cost of existing power quality monitors (Power Quality Measurement, PQM) is generally high, and the cost-effectiveness of building a full-coverage monitoring system is not ideal. Therefore, it is of great practical significance and application value to reduce the number of PQM installations and intelligently select the appropriate installation location, that is, to achieve the best monitoring effect with the least PQM. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a distribution network interference source location method based on monitoring point configuration optimization which is low-cost and does not reduce the accuracy of power quality interference source location.

[0008] The purpose of the present invention can be achieved by the following technical solutions:

[0009] A method for locating a distribution network interference source based on monitoring point configuration optimization comprises the following steps:

[0010] Collecting power quality data of each power quality monitor, wherein the power quality monitor is installed at a plurality of system nodes based on a pre-set monitoring point configuration optimization principle, and the power quality data includes an interference direction determination result;

[0011] Based on the known line data and the collected power quality data, the power quality data of all system nodes within the whole network detection target is estimated;

[0012] Based on the power quality data of all the system nodes, a genetic algorithm is used to obtain an optimal solution, and the interference source position is obtained according to the optimal solution.

[0013] Furthermore, the monitoring point configuration optimization principle includes:

[0014] 1) Install a power quality monitor on the power supply side;

[0015] 2) If there are only N outgoing lines at a certain system node i and no distributed power source, then install power quality monitors on N-1 outgoing lines in descending order of line power;

[0016] 3) If there are system nodes i and j connected in series and no other branches, install a power quality monitor at the outgoing line end of a system node close to the power supply side.

[0017] Furthermore, the power quality data of all system nodes within the whole network detection target are estimated and obtained according to the line voltage drop calculation method.

[0018] Furthermore, the power quality data includes current and voltage, and the interference direction judgment result is determined based on the current and voltage.

[0019] Furthermore, in the process of using the genetic algorithm to solve and obtain the optimal solution, based on the power quality data of all system nodes within the full network detection target, the states of the system lines and system nodes are chromosomally encoded, a fitness evaluation function is established, and the optimal solution that minimizes the fitness evaluation function is calculated.

[0020] Furthermore, the chromosome encoding adopts 0, 1 encoding.

[0021] Furthermore, the encoding of the state of the system line is specifically as follows: the element corresponding to the line with interference source is represented by 1, and the element corresponding to the line without interference source is represented by 0;

[0022] The encoding of the state of the system node is specifically: when the interference direction judgment result is forward interference, it is represented by 1, and when it is backward interference, it is represented by 0.

[0023] Furthermore, the fitness evaluation function is expressed as:

[0024]

[0025] In the formula, C is a set integer, n r is the number of monitoring points, s is the state vector of the system line, r k is the monitoring status of the kth system node, is the expected value function of the interference direction information of the kth monitoring point, A k represents the interference weight of the kth monitoring state.

[0026] Furthermore, the expected value function The expression is:

[0027]

[0028] In the formula, s i is the state of the i-th line upstream of monitoring point k, s j is the status of the jth line downstream of monitoring point k.

[0029] Furthermore, the interference weight A of the kth monitoring state is k The expression is:

[0030]

[0031] In the formula, E dk is the high-frequency interference energy of the kth monitoring point at the end of the interference.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. The cost of existing power quality monitoring devices in the present invention is generally high. For example, the cost-effectiveness of building a full-coverage monitoring system is not ideal. By optimizing the configuration of monitoring points, a certain number of power quality monitoring devices PQM are installed at appropriate locations to meet the conditions for state estimation, so that system nodes without PQM installed can obtain their estimation information through known monitoring data, thereby obtaining power quality data of the detection targets of the entire network, completing the positioning of interference sources, and effectively reducing costs.

[0034] 2. The present invention takes into account the impact of distributed power sources during the operation of the distribution network, improves the interference source positioning optimization model, can quickly and accurately determine the location of the power quality interference source, and improves the power supply reliability of the distribution network. It has strong practical significance and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the process of the present invention;

[0036] Figure 2 This is the electrical diagram of the 110kV Huiping Substation in the embodiment;

[0037] Figure 3 is the interference energy result of M9 in the embodiment;

[0038] Figure 4 is the interference energy result of M9_1 in the embodiment;

[0039] Figure 5 is the interference energy result of M5 in the embodiment;

[0040] Figure 6 is the interference energy result of M8 in the embodiment;

[0041] Figure 7 is the interference energy result of M6 in the embodiment;

[0042] Figure 8 It is the fitness average value change curve in the embodiment;

[0043] Fig. 9 It is the maximum fitness change curve in the embodiment. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0045] The present invention provides a method for locating the position of a distribution network interference source based on monitoring point configuration optimization, establishes a distribution network model containing distributed power sources, and enables system nodes without a power quality monitoring device (PQM) installed to obtain its estimation information through known monitoring data based on the state estimation of the interference source power data. A method for installing a certain number of PQMs at appropriate locations is provided to meet the conditions for state estimation, thereby obtaining the power quality data of the detection target of the entire network. Then, an interference source positioning optimization model is established, and the interference source is located by solving the problem according to an improved genetic algorithm.

[0046] like Figure 1 As shown, the method comprises the following steps:

[0047] S1. Collect power quality data from each power quality monitor, which is installed at several system nodes based on a pre-set monitoring point configuration optimization principle. The power quality data includes current, voltage, and interference direction judgment results determined based on the current and voltage.

[0048] The state estimation method enables system nodes without PQM installed to obtain their estimated information through known monitoring data. Therefore, a certain number of PQMs need to be installed in the system at appropriate locations to meet the conditions for state estimation, thereby obtaining the power quality data of the entire network detection target. In a specific implementation, the monitoring point configuration optimization principles include:

[0049] 1) Install a power quality monitor PQM on the power supply side;

[0050] 2) If there are only N outgoing lines at a certain system node i and no distributed power source, PQMs are installed on N-1 outgoing lines according to the line power from large to small;

[0051] 3) If there are system nodes i and j connected in series and no other branches, install the PQM at the outgoing line end of a system node close to the power supply side.

[0052] S2. Based on the known line data and the collected power quality data, estimate and obtain the power quality data of all system nodes within the whole network detection target.

[0053] In a specific implementation manner, a distribution network model including distributed power sources is established, and power quality data of all system nodes within the entire network detection target is estimated and obtained according to a line voltage drop calculation method.

[0054] S3. Based on the power quality data of all the system nodes, a genetic algorithm is used to obtain an optimal solution, and the location of the interference source is obtained according to the optimal solution.

[0055] In a specific implementation, the position of the interference source relative to the monitor is determined based on the current u and voltage i signals detected by the PQM monitor. Then, based on the coordinates of the monitor and the coordinates of the interference source relative to the monitor, the interference position data of different monitoring points of the PQM monitor are used to obtain the approximate location of the interference source. The positioning method can also be viewed as an optimization global optimization problem. By constructing an objective function, the positioning of the interference source is quantified into a problem of 0 and 1, and it is solved to determine the position of the interference source based on the optimal solution.

[0056] In the process of using genetic algorithm to solve and obtain the optimal solution, based on the power quality data of all system nodes in the whole network detection target, the status of system lines and system nodes is chromosome encoded, a fitness evaluation function is established, and the optimal solution that minimizes the fitness evaluation function is calculated.

[0057] In this embodiment, the chromosome coding adopts 0 and 1 coding. Specifically, the coding of the state of the system line is as follows: the element corresponding to the line with interference source is represented by 1, and the element corresponding to the line without interference source is represented by 0; the coding of the state of the system node is as follows: when the interference direction judgment result is forward interference, it is represented by 1, and when it is backward interference, it is represented by 0.

[0058] In the genetic algorithm, each potential solution is encoded one by one, and then the string formed by the genetic encoding is iterated, that is, the chromosome operation is performed to obtain the optimal solution.

[0059] In a specific implementation, an environmental evaluation function that connects the wire state and the interference direction information of the monitoring instrument is constructed as a fitness evaluation function, and the specific expression is:

[0060]

[0061] In the formula, C is a set integer. In order to ensure that the fitness value is positive, this embodiment takes 1, n r is the number of monitoring points, s is the state vector of the system line, and its dimension is n, which is determined by the number of lines in the system. The elements corresponding to the lines with interference sources are represented by 1, and the elements corresponding to the lines without interference sources are represented by 0. k is the monitoring status of the kth system node. When the interference direction judgment result is forward interference, it is represented by 1, and when it is backward interference, it is represented by 0. is the expected value function of the interference direction information of the kth monitoring point, A k represents the interference weight of the kth monitoring state.

[0062] For a certain monitoring point, if there is no interference source in all upstream lines and there is an interference source in a downstream line, the value of the monitoring function corresponding to this point is 1; if there is an interference source in an upstream line and there is no interference source in all downstream lines, the value of the monitoring function corresponding to this point is 0. Therefore, solve the expected value function of the kth monitoring point for:

[0063]

[0064] In the formula, s i is the state of the i-th line upstream of monitoring point k, 1 if there is an interference source, and 0 if there is no interference source, s j is the status of the jth line downstream of monitoring point k, which is 1 if there is an interference source and 0 if there is no interference source.

[0065] Since the interference direction determination results obtained from monitoring points with weak interference signals are often unreliable, the role of strong signals in locating the interference direction should be highlighted, and higher weights should be set for items corresponding to measurement points with strong interference signals. The method for setting the interference weight A is to add the absolute values ​​of the final values ​​of the high-frequency interference energy of all monitoring points and calculate the proportion of the absolute values ​​of the final values ​​of the high-frequency interference energy of each monitoring point. The interference weight A of the kth detection state is k The expression is:

[0066]

[0067] In the formula, E dk is the high-frequency interference energy of the kth monitoring point at the end of the interference.

[0068] Finally, the genetic operation searches for the optimal solution in the solution space. When the algorithm reaches the maximum number of iterations, the operation ends and the optimal solution is obtained. The location of the interference source is determined based on the 0 and 1 values ​​of each element in the optimal solution, thereby determining the faulty line.

[0069] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0070] In another embodiment, an electronic device is also provided, comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the method for locating the location of a distribution network interference source based on monitoring point configuration optimization as described above.

[0071] Example

[0072] This embodiment takes the 110kV Huiping Substation in a certain area as an example.

[0073] There are two main transformers in the 110kV Huiping Station in a certain area. The wiring in the station adopts a single-mother segmented method. There are 28 10kV lines in total, of which 7 are backup lines. There are two incoming lines, namely Yihui 1367 and Yihui 1368. Under normal circumstances, the active power of the station is about 9.5MW. The main wiring diagram in the station is as follows: Figure 2 As shown in Table 1. The new energy data adopts Shanghai XX photovoltaic new energy, with a capacity of 2500kW and a grid-connected voltage level of 10kV. The photovoltaic is connected to the 10kV Hui6 Jiacheng Line A of Huiping Station. The power quality detector is installed at the head end of the line, and its number is marked according to the line sequence, as shown in Table 1. The number of the power quality monitor of the switch station is the line number plus _1.

[0074] Table 1 110kV Huiping Station Line and its Monitoring Instrument Number

[0075]

[0076]

[0077] A three-phase short circuit fault occurred on the Hui 9 Dongfang Mingdi A line at 0.1s-0.2s, with a frequency of 49500Hz. The monitor at the head end of the Hui 9 Dongfang Mingdi A line is numbered M9, and the monitor corresponding to its switch station is M9_1. The monitor at the head end of the Hui 5 Huipingbei line is numbered M5, and the monitor at the head end of the Hui 8 Nanxiang Zhidi A line is numbered M8. According to the previous interference source direction determination principle, the fault point is downstream of M9 and upstream of M9_1, M5, and M8. Therefore, in theory, the interference energy of M9 should be negative, and the interference energy of M9_1, M5, and M8 should be positive. Finally, the interference energies of M9, M9_1, M5, and M8 are as follows: Figures 3 to 6 As shown in the figure, the results are consistent with the theory.

[0078] According to the principle of optimal configuration of power quality monitoring points proposed by the above method, combined with the actual network structure of Huiping Station, since there are two transformers in Huiping Station, two transformers with 1 and 2 outgoing lines respectively are selected without power quality monitors installed. When one outgoing line is not equipped with a power quality monitor, it can be randomly selected. When two outgoing lines are not equipped with power quality monitors, the outgoing line with smaller load should be selected. Therefore, it is set that among all the incoming and outgoing lines of Huiping Station, only Hui 1 Huiping East, Hui 15 Jiamei, and Hui 21 Jiacheng Yi are not equipped with power quality monitors. When the operating status of all power quality monitors is accurate, the status of the power quality monitor at this time is that the detectors MT1 and M9 detect downstream disturbances, and the status of its power quality detectors is shown in Table 2.

[0079] Table 2 Power quality monitor status

[0080]

[0081]

[0082] The interference weight of each monitoring point is:

[0083] A=[0.2437,0.0336,0.0021,0.0054,0.0163,0.0254,0.0011,0.2590,0 .2724,0.0009,0.0032,0.0124,0.0687,0.0004,0.0016,0.0025,0.0003 ,0.0036,0.0037,0.0045,0.0070,0.0072,0.0021,0.0017,0.0035,0.0 037,0.0017,0.0017,0.0026,0.0026,0.0017,0.0017,0.0010,0.0009].

[0084] At this time, through the genetic algorithm, the initial chromosome is 20, iterates 100 times, and the crossover probability P cis 0.5, the mutation probability P m When the fitness value is set to 0.05, the fault location result can be obtained. In the line state with a fitness value of 1, the line state of the 9th line is 1, marking that the 9th line has a fault, and the interference source is accurately located as Hui 9 Dongfang Mingdi A Line, which is consistent with the fault information.

[0085] In order to verify the fault-tolerant performance of the interference source location method based on genetic algorithm proposed in this project, the following experiment is conducted. Under normal circumstances, that is, when there is no misjudgment, the fault point is located upstream of the monitoring point M6, and the interference energy is positive. However, due to the addition of photovoltaic power supply to the line where the monitoring point M6 is located, the monitoring point is misjudged, and the interference energy obtained is negative. Figure 7 As shown. Using the genetic algorithm, the initial chromosome is set to 20, the iteration is 100 times, and the crossover probability P c is 0.5, the mutation probability P m The value of the genetic algorithm is 0.05, and the number of individuals in each generation is limited to 20. The genetic algorithm is calculated 1000 times, and the final accuracy is 93.8%. The average fitness curve is as follows: Figure 8 As shown, its maximum fitness curve is as follows Fig. 9 shown.

[0086] Depend on Figure 8 , Fig. 9 It can be seen that when the genetic algorithm is used to locate the power quality interference source, the optimal solution for the interference source location appears around the 20th generation, the maximum value of the fitness begins to stabilize, and the average value of the fitness is maintained above 0.9. This fully demonstrates the characteristics of the power quality monitoring point optimization principle in solving problems quickly and with high stability.

[0087] According to the power quality monitoring point optimization principle proposed in the previous article, the power quality monitors on the outgoing lines with smaller loads are reduced, and at the same time, according to the state estimation method, the number of reductions is balanced on each bus, and the interference source positioning accuracy is calculated when 3, 5, 7, and 9 power quality monitors are missing. When 3 power quality monitors are missing in the system, that is, the number of monitors required to be installed in the system is 29, and the lines where the monitors are installed account for 90.6% of the total lines, in the case of a misjudgment of the outgoing line of one power quality monitor, the accuracy of locating the interference source is at least 81.3%. When 9 power quality monitors are missing in the system, that is, the lines where the monitors are installed in the system account for 71.9% of the total lines, in the case of a misjudgment of the outgoing line of one power quality monitor, the accuracy of locating the interference source is still maintained at about 80%, indicating that the monitoring point configuration optimization principle proposed in the present invention has strong reliability in locating power quality interference sources.

[0088] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for locating the interference source of a distribution network based on monitoring point configuration optimization, characterized in that: The following steps are involved: Collecting power quality data of each power quality monitor, wherein the power quality monitor is installed at a plurality of system nodes based on a pre-set monitoring point configuration optimization principle, and the power quality data includes an interference direction determination result; Based on the known line data and the collected power quality data, the power quality data of all system nodes within the whole network detection target is estimated; Based on the power quality data of all the system nodes, a genetic algorithm is used to obtain an optimal solution, and a location of the interference source is obtained according to the optimal solution; The monitoring point configuration optimization principles include: 1) Install a power quality monitor on the power supply side; 2) If there are only N outgoing lines at a certain system node i and no distributed power source, then install power quality monitors on N-1 outgoing lines in descending order of line power; 3) If there are system nodes i and j connected in series and no other branches, install a power quality monitor at the outgoing line end of a system node close to the power supply side.

2. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 1 is characterized in that: The power quality data of all system nodes within the whole network detection target are estimated based on the line voltage drop calculation method.

3. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 1 is characterized in that: The power quality data includes current and voltage, and the interference direction judgment result is determined based on the current and voltage.

4. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 1 is characterized in that: In the process of using genetic algorithm to solve and obtain the optimal solution, based on the power quality data of all system nodes in the whole network detection target, the status of system lines and system nodes is chromosome encoded, a fitness evaluation function is established, and the optimal solution that minimizes the fitness evaluation function is calculated.

5. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 4 is characterized in that: The chromosome encoding adopts 0, 1 encoding.

6. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 5 is characterized in that: The encoding of the state of the system line is specifically: the element corresponding to the line with interference source is represented by 1, and the element corresponding to the line without interference source is represented by 0; The encoding of the state of the system node is specifically: when the interference direction judgment result is forward interference, it is represented by 1, and when it is backward interference, it is represented by 0.

7. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 4 is characterized in that: The expression of the fitness evaluation function is: In the formula, C is a set integer, n r is the number of monitoring points, s is the state vector of the system line, r k is the monitoring status of the kth system node, is the expected value function of the interference direction information of the kth monitoring point, A k represents the interference weight of the kth monitoring state.

8. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 7 is characterized in that: The expected value function The expression is: In the formula, s i is the state of the i-th line upstream of monitoring point k, s j is the status of the jth line downstream of monitoring point k.

9. The method for locating the interference source of the distribution network based on the optimization of the monitoring point configuration according to claim 7 is characterized in that: The interference weight A of the kth monitoring state k The expression is: In the formula, E dk is the high-frequency interference energy of the kth monitoring point at the end of the interference.

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