An Internet of Things-based power distribution and consumption energy efficiency management method and system

Abstract

A power distribution and consumption energy efficiency management method and system based on the Internet of Things according to the present invention belong to the technical field of power distribution and consumption energy efficiency management, and include: S1: obtaining real-time power distribution data, rated device data, and device connection relationships of power distribution devices in a power distribution area; S2: comparing the real-time power distribution data of the power distribution devices with the rated device data to obtain a normal load set and an over / under load set; S3: constructing a node matrix; S4: calculating a load balance degree; S5: calculating an energy efficiency matching degree; S6: forming a matching matrix; inputting the matching matrix into the KM algorithm to obtain a maximum weight matrix; S7: calculating a system fitness; comparing the system fitness with a fitness threshold to determine whether the maximum weight matrix is reliable; if it is reliable, outputting the maximum weight matrix; otherwise, repeating S4-S7 until the maximum weight matrix is reliable, and then outputting the maximum weight matrix at this time. This solution and system can reduce the number of power distribution times and the power distribution distance, and reduce losses.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution and consumption energy efficiency management, and particularly relates to a power distribution and consumption energy efficiency management method and system based on the Internet of Things. Background Art

[0002] The power distribution system is an important part of the power system. It is responsible for safely and efficiently distributing electric energy from the high-voltage transmission network to the user side. It is not only used to connect the upper-level transmission cable, the lower-level main transformer and the power distribution equipment, but also provides corresponding countermeasures in a timely manner when emergencies occur in the power distribution equipment.

[0003] The existing power distribution management methods mainly collect the electricity consumption of users and provide the best power distribution plan after analyzing the electricity consumption. This method conducts power distribution management at the user side, ignoring the line energy consumption problems caused by power distribution to each user end. For example, the existing power distribution management methods will distribute power to multiple power distribution devices that are close to each other separately, resulting in multiple line losses, thereby affecting the power distribution efficiency.

[0004] Therefore, it is necessary to effectively supply power to multiple power distribution devices with connection relationships at the same time in combination with the connection relationships between the power distribution devices, reduce the number of power distribution times, thereby reducing the line losses caused by multiple power distribution times and improving the power distribution efficiency. Summary of the Invention

[0005] The present invention proposes a power distribution and consumption energy efficiency management method and system based on the Internet of Things to solve the above problems.

[0006] To solve the above technical problems, the present invention proposes the following solutions: A power distribution and consumption energy efficiency management method based on the Internet of Things, including the following steps:

[0007] S1: Obtain the real-time power distribution data, equipment rated data and equipment connection relationships of all power distribution devices in the power distribution area;

[0008] S2: Compare the real-time power distribution data of the power distribution devices with the equipment rated data, and divide them into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results; assume that there are X power distribution devices in the power distribution area, including M non-abnormal power distribution devices and Y abnormal power distribution devices; divide the M non-abnormal power distribution devices into a normal load set and an over / under load set, and assume that there are M1 normal load devices and M2 over / under load devices;

[0009] S3: Construct a node matrix according to the connection relationships between the normal load devices in the normal load set and the over / under load devices in the over / under load set;

[0010] S4: Select Mx normal load devices from the normal load set, where 0 < Mx < M1; select My over / under load devices from the over / under load set; 0 < My < M2; calculate the load balance degree corresponding to the Mx normal load devices and the My over / under load devices.

[0011] S5: Calculate the energy efficiency matching degree corresponding to the Mx normal load devices and the My over / under load devices according to the load balance degree corresponding to the Mx normal load devices and the My over / under load devices.

[0012] S6: Place the energy efficiency matching degree of the corresponding normal load devices and over / under load devices at the corresponding positions in the node matrix to form a matching matrix; input the matching matrix into the KM algorithm to obtain the maximum weight matrix.

[0013] S7: Calculate the system fitness of the maximum weight matrix; set a fitness threshold, compare the system fitness with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison result; if the system fitness is greater than or equal to the fitness threshold, the maximum weight matrix is reliable, and output the maximum weight matrix; if the system fitness is less than the fitness threshold, the maximum weight matrix is unreliable, re-select Mx normal load devices from the normal load set and My over / under load devices from the over / under load set; repeat S4 - S7, generate another maximum weight matrix subsequently, continuously calculate the system fitness of the subsequent maximum weight matrix, compare the system fitness of the subsequent maximum weight matrix with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison result, until the maximum weight matrix is reliable and then stop looping S4 - S7, and output the maximum weight matrix at this time.

[0014] Further, step S2 includes the following steps:

[0015] S2.1: For a certain power distribution device, its real-time power distribution data includes: real-time voltage Vt, real-time current It, real-time temperature value Tt, and real-time ambient temperature value Te; its rated device data includes: rated voltage V rated , rated current I rated , device temperature threshold T th , and device ambient temperature value threshold T m ;

[0016] If Vt ≤ V rated , and at the same time It ≤ I rated , Tt ≤ T th , and Te ≤ T m , then this power distribution device is a non-abnormal power distribution device; otherwise, this power distribution device is an abnormal power distribution device.

[0017] S2.2: For any non-abnormal power distribution device, calculate its real-time power Pt, Pt = Vt * It; calculate its rated power Prated , P rated = V rated* I rated ; If , then this non - abnormal power distribution device is a normal load device, and this normal load device is classified into the normal load concentration; if or , then this non - abnormal power distribution device is an over - / under - load device, and this over - / under - load device is classified into the over - / under - load concentration.

[0018] Furthermore, step S3 includes the following steps:

[0019] S3.1: Number all the normal load devices in the normal load concentration; number all the over - / under - load devices in the over - / under - load concentration;

[0020] Set the numbers of M1 normal load devices as: N 1 , N 2 , ……, N k , ……N M1 ; k ∈ [1, M1], k is an integer;

[0021] Set the numbers of M2 over - / under - load devices as: H 1 , H 2 , ……, H f , ……H M2 ; f ∈ [1, M2], f is an integer;

[0022] S3.2: Based on the connection relationship between the normal load devices and the over - / under - load devices, establish a node matrix;

[0023] The constructed node matrix is expressed as:

[0024]

[0025] Matrix A in represents the connection relationship between the normal load device numbered N k and the over - / under - load device numbered H f ; if there is a connection relationship between N k and H f , then is recorded as "1"; if there is no connection relationship between N k and H f , then is recorded as "0".

[0026] Furthermore, in step S4, the method of selecting Mx normal load devices from the normal device concentration and My over - / under - load devices from the over - / under - load concentration is random selection;

[0027] The load balancing degree formula corresponding to Mx normal load devices and My ultra-low / high load devices is as follows:

[0028]

[0029] In formula (1), represents the real-time power of the normal load device numbered N k ;

[0030] represents the real-time power of the ultra-low / high load device numbered H f ;

[0031] represents the average real-time power of the normal load device numbered N k and the ultra-low / high load device numbered H f ;

[0032] s represents the load balancing degree of the normal load device numbered N k and the ultra-low / high load device numbered H f .

[0033] Furthermore, in step S5, the calculation formula for the energy efficiency matching degree is as follows:

[0034]

[0035] In formula (2), represents the energy efficiency matching degree between the normal load device numbered N k and the ultra-low / high load device numbered H f ;

[0036] are both preset coefficients;

[0037] s represents the load balancing degree of the normal load device numbered N k and the ultra-low / high load device numbered H f ;

[0038] P loss represents the total line loss generated by supplying power to the normal load device numbered N k and the ultra-low / high load device numbered H f ;

[0039] d represents the total distance of supplying power to the normal load device numbered N k and the ultra-low / high load device numbered H f .

[0040] Further, in step S7, the formula for system fitness is as follows:

[0041]

[0042] In formula (3), F is the system fitness of the maximum weight matrix;

[0043] represents the real-time power of the i-th power distribution device among the selected power distribution devices; i ∈ [1, Mx + My];

[0044] represents the rated power of the i-th power distribution device among the selected power distribution devices;

[0045] Mx + My represents the number of power distribution devices selected in S4;

[0046] represents the equipment power utilization coefficient;

[0047] represents the power supply reliability coefficient; ;

[0048] represents the power supply reliability index of the i-th power distribution device, .

[0049] Further, an energy efficiency management system for power distribution and utilization based on the Internet of Things includes the following modules:

[0050] A data acquisition module for acquiring the real-time power distribution data, equipment rated data, and equipment connection relationships of power distribution devices in the power distribution area;

[0051] An equipment classification module for comparing the real-time power distribution data of power distribution devices with their equipment rated data, and classifying the power distribution devices in the power distribution area into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results, and classifying the non-abnormal power distribution devices into a normal load set and an over / under load set;

[0052] A matrix construction module for numbering the normal load devices in the normal load set and numbering the over / under load devices in the over / under load set, and constructing a node matrix according to the connection relationships between the normal load devices in the normal load set and the over / under load devices in the over / under load set;

[0053] An energy efficiency matching degree calculation module for selecting some normal load devices from the normal load set and some over / under load devices from the over / under load set, and calculating the energy efficiency matching degree between the selected normal load devices and the corresponding over / under load devices;

[0054] A matching matrix generation module for placing the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix;

[0055] The maximum weight calculation module is used to perform maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix;

[0056] The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is reliable according to the comparison result. If it is reliable, the maximum weight matrix is output. If it is not reliable, the energy efficiency matching degree calculation module is used to reselect the normal load equipment and over / under load equipment, and recalculate the energy efficiency matching degree.

[0057] Adopting the above solution, this solution can achieve the following beneficial effects:

[0058] 1. First, the present invention classifies according to the load conditions of the equipment, and then constructs a node matrix based on the equipment connection relationship. Then, the energy efficiency matching degree between the equipment is calculated to ensure a more reasonable matching between the equipment. The equipment combination with a high matching degree can reduce line losses, improve power supply efficiency, and avoid unnecessary energy consumption between the equipment. Then, the KM algorithm is used for maximum weight allocation to obtain the optimal matching solution. The method provided by the present invention can effectively improve the matching degree between the equipment and ensure that the energy efficiency of the entire power distribution system reaches the optimal. By optimizing the power consumption relationship and matching solution between the equipment, the energy efficiency of the entire power distribution system is improved, unnecessary energy consumption is reduced, and thus the waste of electric energy is effectively reduced.

[0059] 2. The power distribution system supplies power to two power distribution devices simultaneously. Compared with the power distribution system supplying power to each power distribution device separately, the number of power distribution times is reduced, thereby reducing the losses on the power distribution line.

[0060] 3. Two power distribution devices with a high matching degree are matched. When the power distribution system distributes power to them, the power distribution distance is effectively reduced, thereby reducing the losses on the power distribution line. Description of the Drawings

[0061] Figure 1 It is a flowchart of a power consumption energy efficiency management method based on the Internet of Things. Detailed Embodiments

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] Embodiment 1: See Figure 1, An energy efficiency management method for power distribution and consumption based on the Internet of Things, comprising the following steps:

[0064] S1: Obtain the real-time power distribution data, rated device data, and device connection relationships of all power distribution devices within the power distribution area.

[0065] For example, assume there are X power distribution devices in the power distribution area, and obtain the real-time power distribution data, rated device data, and device connection relationships of the X power distribution devices based on the Internet of Things.

[0066] Among them, the real-time power distribution data of the power distribution device includes: real-time voltage, real-time current, real-time temperature value, and real-time ambient temperature value.

[0067] The rated device data includes: rated voltage, rated current, temperature threshold, and ambient temperature value threshold.

[0068] The device connection relationship includes: physical connection or communication connection between power distribution devices. Among them, the real-time power distribution data is obtained by different sensors collecting real-time data, the rated device data is obtained by collecting the data on the identification plates of power distribution devices, and the device connection relationship is obtained from the topological structure of power distribution devices. The above-obtained data and connection relationships are all transmitted to the power distribution system based on the Internet of Things.

[0069] S2: Compare the real-time power distribution data of the power distribution device with the rated device data, and divide them into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results; divide the non-abnormal power distribution devices into normal load sets and over / under load sets.

[0070] S2 specifically includes the following steps:

[0071] S2.1: Compare the real-time voltage Vt, real-time current It, real-time temperature value Tt, and real-time ambient temperature value Te of a certain power distribution device with the corresponding rated voltage V rated , rated current I rated , device temperature threshold T th and device ambient temperature value threshold T m respectively; for this power distribution device, if Vt ≤ V rated , and at the same time It ≤ I rated , Tt ≤ T th and Te ≤ T m , then this power distribution device is a non-abnormal power distribution device. Otherwise, this power distribution device is an abnormal power distribution device.

[0072] S2.2: Remove the abnormal power distribution devices from the power distribution area and retain the non-abnormal power distribution devices; for the non-abnormal power distribution devices, divide them into normal load sets and over / under load sets according to the classification criteria.

[0073] Assume that among X power distribution devices, there are Y abnormal power distribution devices and M non-abnormal power distribution devices, then M = X - Y.

[0074] For a certain non-abnormal power distribution device, calculate its real-time power Pt, Pt = Vt * It; calculate its rated power P rated , then P rated = V rated* I rated ;

[0075] The classification criterion is:

[0076] For a certain non-abnormal power distribution device, if , then it is a normal load device, and this normal load device belongs to the normal load concentration.

[0077] If or , then it is an over- / under-load device and belongs to the over- / under-load concentration.

[0078] S3: Construct a node matrix according to the connection relationship between the normal load devices in the normal load concentration and the over- / under-load devices in the over- / under-load concentration.

[0079] S3 specifically includes the following steps:

[0080] S3.1: Count the number of normal load devices in the normal load concentration, and number all the normal load devices in the normal load concentration; count the number of over- / under-load devices in the over- / under-load concentration, and number all the over- / under-load devices in the over- / under-load concentration.

[0081] Assume that in the normal load concentration, there are M1 normal load devices; in the over- / under-load concentration, there are M2 over- / under-load devices, and M = M1 + M2.

[0082] Number the M1 normal load devices, and set the numbers as: N 1 , N 2 , ……, N k , ……N M1 ; k ∈ [1, M1], k is an integer.

[0083] Number the M2 over- / under-load devices, and set the numbers as: H 1 , H 2 , ……, H f , ……H M2 ; f ∈ [1, M2], f is an integer.

[0084] S3.2: Based on the connection relationship between the normal load devices and the over- / under-load devices, establish a node matrix.

[0085] The constructed node matrix is expressed as:

[0086]

[0087] Matrix A in represents the connection relationship between the normal load device numbered N k and the over / under load device numbered H f ; if there is a connection relationship between N k and H f , then it is recorded as "1"; if there is no connection relationship between N k and H f , then it is recorded as "0".

[0088] S4: Select Mx normal load devices from the normal load set, where 0 < Mx < M1; select My over / under load devices from the over / under load set; 0 < My < M2; calculate the load balance degree corresponding to the Mx normal load devices and the My over / under load devices.

[0089] In this embodiment, the method of selecting Mx normal load devices and selecting My over / under load devices is random selection.

[0090] The formula for the load balance degree is:[[]]

[0091]

[0092] In formula (1), represents the real-time power of the normal load device numbered N k ;

[0093] represents the real-time power of the over / under load device numbered H f ;

[0094] represents the average real-time power of the normal load device numbered N k and the over / under load device numbered H f ;

[0095] s represents the load balance degree of the normal load device numbered N k and the over / under load device numbered H f .

[0096] From formula (1), the load balance degree between any one of the Mx normal load devices and any one of the My over / under load devices can be calculated.

[0097] S5: Calculate the energy efficiency matching degree between any normal load device in Mx and any over / under load device in My according to the load balancing degree between any normal load device in Mx and any over / under load device in My.

[0098] The formula for the energy efficiency matching degree is:

[0099]

[0100] In formula (2), represents the normal load device numbered N k and the energy efficiency matching degree between the over / under load device numbered H f ;

[0101] are all preset coefficients;

[0102] s represents the load balancing degree between the normal load device numbered N k and the over / under load device numbered H f ;

[0103] P loss represents the total line loss generated by power supply to the normal load device numbered N k and the over / under load device numbered H f ;

[0104] The line loss is proportional to the square of the line current, the line resistance, and the line length, and is calculated after obtaining values such as the line current, line resistance, and line length.

[0105] d represents the total distance of power supply to the normal load device numbered N k and the over / under load device numbered H f ;

[0106] The load balancing degree between any normal load device in Mx and any over / under load device in My can be calculated from formula (2).

[0107] S6: Place the energy efficiency matching degrees of the corresponding normal load devices and over / under load devices at the corresponding positions in the node matrix to form a matching matrix; input the matching matrix into the KM algorithm to obtain the maximum weight matrix.

[0108] Because the energy efficiency matching degree is the energy efficiency matching degree between the normal load device numbered N k and the over / under load device numbered H f ;

[0109] So place at the At the position; when setting up the matching matrix, the elements in the matching matrix should satisfy:

[0110] If in the node matrix is equal to "1", then will be placed at the position in the node matrix, replacing the "1" at the original position;

[0111] Otherwise, the value at the position in the node matrix will not be changed.

[0112] Steps S4 - S6 are illustrated as follows: Assume there are 5 normal load devices in the power distribution area, denoted as: N 1 、N 2 、N 3 、N 4 、N 5 ;

[0113] 4 over / under load devices, denoted as: H 1 、H 2 、H 3 、H 4 ;

[0114] Assume the connection relationships between the 5 normal load devices and the 4 over / under load devices are: N 1 is connected to H 1 and H 3 ; N 2 is connected to H 2 and H 4 ; N 3 is connected to H 1 ; N 4 has no connection with other power distribution devices; N 5 is connected to H 2 ;

[0115] Then, according to the above corresponding connection relationships, the constructed node matrix A 1 is:

[0116]

[0117] Randomly select 4 normal load devices from the 5 normal load devices. Assume the selected normal load devices are: N 1 、N 2 、N 3 、N 4 ; Randomly select 4 normal load devices from the 5 normal load devices. Assume the selected normal load devices are: N 1 、N 2 、N 3 、N4 ;

[0118] Randomly select 3 ultra / low load devices from 4 ultra / low load devices. Assume the selected ultra / low load devices are: H 1 , H 2 , H 3 ;

[0119] According to Equation (1) and Equation (2), calculate the energy efficiency matching degrees of the 4 selected normal load devices corresponding to the 3 selected ultra / low load devices. Assume: N 1 The energy efficiency matching degrees with H 1 , H 2 , H 3 are 0.9, 0.7, and 0.5 respectively. N 2 The energy efficiency matching degrees with H 1 , H 2 , H 3 are: 0.7, 0.6, and 0.3 respectively. N 3 The energy efficiency matching degrees with H 1 , H 2 , H 3 are: 0.8, 0.5, and 0.3 respectively. N 4 The energy efficiency matching degrees with H 1 , H 2 , H 3 are 0.4, 0.3, and 0.2.

[0120] Place the corresponding energy efficiency matching degrees into the node matrix A 1 to obtain the matching matrix E 1 as:

[0121]

[0122] After inputting the matching matrix E 1 into the KM algorithm, the KM algorithm performs the maximum weight allocation operation on the matching matrix E 1 to obtain the maximum weight matrix.

[0123] For example: After inputting the matching matrix E 1 into the KM algorithm, based on the maximum weight allocation principle, the obtained maximum weight matrix G 1 is:

[0124]

[0125] Brief description of the allocation principle: Take the matching matrix E1 Looking at the first row, the matching degree between N 1 and H 1 is higher than that between N 1 and H 2 and H 3 and H 4 respectively. Therefore, in the maximum weight matrix G 1 the first element in the first row is "1" and the others are "0", indicating that N 1 matches H 1 .

[0126] Taking the matching matrix E 1 Looking at the second row, the matching degree between N 2 and H 4 is higher than that between N 2 and H 1 and H 2 and H 3 respectively. Therefore, in the maximum weight matrix G 1 the fourth element in the second row is "1" and the others are "0".

[0127] Taking the matching matrix E 1 Looking at the fourth row, since all are "0", it indicates that N 4 does not match H 1 and H 2 and H 3 and H 4 respectively; all elements in the fourth row of the maximum weight matrix G 1 are "0".

[0128] Taking the matching matrix E 1 Looking at the fifth row, the matching degree between N 5 and H 2 is higher than that between N 5 and H 1 and H 3 and H 4 respectively. Therefore, in the maximum weight matrix G 1 the second element in the fifth row is "1" and the others are "0".

[0129] Taking the matching matrix E 1 Looking at the third row, since N 1 and N 2 and N 4 have all been matched, H 3 can only be assigned to N 3 . Therefore, in the maximum weight matrix G1 The third element in the third row is "1", and the others are "0".

[0130] So far, the maximum weight matrix of the 4 selected normal load devices and the 3 selected over / under load devices is obtained G 1 , according to the matching rules in the maximum weight matrix G 1 In the power distribution system, two power distribution devices with high matching degree are simultaneously powered, that is: the power distribution system supplies power to N 1 and H 1 , N 2 and H 4 , N 3 and H 3 or N 5 and H 2 in any one of the combinations simultaneously; supply power to N 4 alone.

[0131] S7: Calculate the system fitness of the maximum weight matrix; set the fitness threshold, compare the system fitness with the fitness threshold, and judge whether the maximum weight matrix is reliable;

[0132] If it is reliable, output the maximum weight matrix;

[0133] If it is not reliable, re-select Mx normal load devices from the normal load set, and re-select My over / under load devices from the over / under load set; repeat S4 - S7, generate another maximum weight matrix subsequently, continuously calculate the system fitness of the subsequent maximum weight matrix, compare the system fitness of the subsequent maximum weight matrix with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison result, until the maximum weight matrix is reliable and then stop looping S4 - S7, and output the maximum weight matrix at this time.

[0134] S7 specifically includes the following steps:

[0135] S7.1: Calculate the system fitness of the maximum weight matrix.

[0136] The formula for the system fitness is:

[0137]

[0138] In formula (3), F is the system fitness of the maximum weight matrix;

[0139] represents the real-time power of the i-th power distribution device among the selected power distribution devices; i ∈ [1, Mx + My]. The i-th power distribution device may be a normal load device or an over / under load device.

[0140] It represents the rated power of the i-th power distribution device among the selected power distribution devices; i ∈ [1, Mx + My].

[0141] Mx + My represents the number of power distribution devices selected in S4;

[0142] It represents the equipment power utilization coefficient;

[0143] It represents the power supply reliability coefficient; 。

[0144] It represents the power supply reliability index of the selected i-th power distribution device. In this embodiment, that is, during the statistical time domain, the ratio of the power supply duration of the i-th power distribution device to the duration of the statistical time domain. ; The duration of the statistical time domain is selected by the power supply unit, such as: one month.

[0145] S7.2: Compare the system fitness of the maximum weight matrix with the fitness threshold. If the system fitness is greater than or equal to the set fitness threshold D , then the maximum weight matrix is reliable, and output the maximum weight matrix;

[0146] If the system fitness is less than the set fitness threshold D , then the maximum weight matrix is unreliable. Re-select Mx normal load devices from the normal load set in S4, and re-select My over / under load devices from the over / under load set in S4; Repeat S4 - S7 until the maximum weight matrix is reliable, and output the corresponding maximum weight matrix.

[0147] Embodiment 2: An Internet of Things-based power distribution and consumption energy efficiency management system includes the following modules:

[0148] The data acquisition module is used to acquire the real-time power distribution data, equipment rated data, and equipment connection relationship of power distribution devices in the power distribution area;

[0149] The equipment classification module is used to compare the real-time power distribution data of the power distribution devices with their equipment rated data. According to the comparison results, classify the power distribution devices in the power distribution area into abnormal power distribution devices and non-abnormal power distribution devices, and classify the non-abnormal power distribution devices into a normal load set and an over / under load set;

[0150] The matrix construction module is used to number the normal load devices in the normal load set and number the over / under load devices in the over / under load set. According to the connection relationship between the normal load devices in the normal load set and the over / under load devices in the over / under load set, construct a node matrix;

[0151] The energy efficiency matching degree calculation module is used to select some normal load devices from the normal load set, select some over / under load devices from the over / under load set, and calculate the energy efficiency matching degree between the selected normal load devices and the corresponding over / under load devices;

[0152] The matching matrix generation module is used to place the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix;

[0153] The maximum weight calculation module is used to perform the maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix;

[0154] The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is reliable according to the comparison result. If it is reliable, the maximum weight matrix is output. If it is not reliable, the energy efficiency matching degree calculation module is used to reselect the normal load devices and over / under load devices and recalculate the energy efficiency matching degree.

[0155] Inspired by the ideal embodiments of the present invention described above, through the above description, relevant staff can completely make various changes and modifications without departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for managing power distribution efficiency based on the Internet of Things, characterized in that: It includes the following steps: S1: Obtain the real-time power distribution data, rated device data, and device connection relationships of all power distribution devices within the power distribution area; S2: Compare the real-time power distribution data of the power distribution devices with the rated device data, and classify them into abnormal power distribution devices and non-abnormal power distribution devices according to the comparison results. Assume there are X power distribution devices in the power distribution area, among which M are non-abnormal power distribution devices and Y are abnormal power distribution devices. Divide the M non-abnormal power distribution devices into a normal load set and an over / under load set. Assume there are M1 normal load devices and M2 over / under load devices; S3: Construct a node matrix based on the connection relationships between the normal load devices in the normal load set and the over / under load devices in the over / under load set; S4: Select Mx normal load devices from the normal load set, where 0 < Mx < M1; select My over / under load devices from the over / under load set; 0 < My < M2; Calculate the load balance degree corresponding to the Mx normal load devices and the My over / under load devices; S5: Calculate the energy efficiency matching degree corresponding to the Mx normal load devices and the My over / under load devices according to the load balance degree corresponding to the Mx normal load devices and the My over / under load devices; S6: Place the energy efficiency matching degrees of the corresponding normal load devices and over / under load devices in the corresponding positions of the node matrix to form a matching matrix; Input the matching matrix into the KM algorithm to obtain the maximum weight matrix; S7: Calculate the system fitness of the maximum weight matrix; Set a fitness threshold, compare the system fitness with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison results; If the system fitness is greater than or equal to the fitness threshold, the maximum weight matrix is reliable, and output the maximum weight matrix; If the system fitness is less than the fitness threshold, the maximum weight matrix is unreliable. Re-select Mx normal load devices from the normal load set and My over / under load devices from the over / under load set; Repeat S4 - S7, and then generate another maximum weight matrix. Continuously calculate the system fitness of the subsequent maximum weight matrix, compare the system fitness of the subsequent maximum weight matrix with the fitness threshold, and judge whether the maximum weight matrix is reliable according to the comparison results. Stop looping S4 - S7 until the maximum weight matrix is reliable, and output the maximum weight matrix at this time; In step S5, the formula for calculating the energy efficiency matching degree is: ； In formula (2), Indicates the number is N k The normal load equipment is H f Energy efficiency matching of over / under load equipment; All are preset coefficients; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded equipment; P loss Indicates that the number is N k Normal load equipment and number H f The sum of line losses caused by powering over / underloaded equipment; d Indicates that the number is N k Normal load equipment and number H f Total distances for over / under load equipment power supply.

2. The method for managing power distribution efficiency based on the Internet of Things according to claim 1, characterized in that: Step S2 includes the following steps: S2.1: For a certain power distribution equipment, its real-time power distribution data includes: real-time voltage Vt, real-time current It, real-time temperature value Tt and real-time ambient temperature value Te; its equipment rated data includes: rated voltage V rated , Rated current I rated , Equipment temperature threshold T th And the device ambient temperature threshold T m ; If Vt≤V rated , and at the same time It≤I rated , Tt≤T th and Te≤T m , then the power distribution equipment is a normal power distribution equipment; otherwise, the power distribution equipment is an abnormal power distribution equipment; S2.2: For any non-abnormal power distribution equipment, calculate its real-time power Pt, Pt=Vt*It; calculate its rated power P rated , P rated =V rated* I rated ;like , then the non-abnormal distribution equipment is a normal load equipment, and the normal load equipment is classified as a normal load set; if or , then, the non-abnormal power distribution equipment is an over / under load equipment, and the over / under load equipment is classified into the over / under load concentration.

3. The method for managing power distribution efficiency based on the Internet of Things according to claim 2, characterized in that: Step S3 includes the following steps: S3.1: Number all the normal load devices in the normal load set; Number all the over / under load devices in the over / under load set; Set the numbers of M1 normal load devices to: N1, N2, ..., N k , ... N M1 ; k∈[1,M1], k is an integer; Set the M2 over / under load equipment numbers to: H1, H2, ..., H f , ...H M2 ; f∈[1,M2], f is an integer; S3.2: Establish a node matrix based on the connection relationships between the normal load devices and the over / under load devices; The constructed node matrix is expressed as: ； matrix A middle Indicates the number is N k Normal load equipment, and number H f The connection relationship of the over / under load equipment; if N k With H f If there is a connection relationship, Recorded as "1"; if N k With H f If there is no connection relationship, Recorded as "0".

4. The method for managing power distribution efficiency based on the Internet of Things according to claim 3 is characterized in that: In step S4, the method of selecting Mx normal load devices from the normal device set and My over / under load devices from the over / under load set is random selection; The formula for the load balance degree corresponding to the Mx normal load devices and the My over / under load devices is: ； In formula (1), Indicates the number is N k Real-time power of normal load equipment; Indicates the number is H f Real-time power of over / under load equipment; Indicates the number is N k Normal load equipment and number H f Real-time power average of over / underload equipment; s Indicates the number is N k Normal load equipment and number H f Load balancing of over / underloaded devices.

5. The method for managing power distribution efficiency based on the Internet of Things according to claim 4 is characterized in that: In step S7, the formula for the system fitness is: ； In formula (3), F is the system fitness of the maximum weight matrix; Represents the real-time power of the i-th power distribution device among the selected power distribution devices; i∈[1,Mx+My]; represents the rated power of the i-th power distribution equipment among the selected power distribution equipment; Mx + My represents the number of power distribution devices selected in S4; Expressed as equipment power utilization factor; Expressed as power supply reliability coefficient; ; It is expressed as the power supply reliability index of the i-th distribution equipment, .

6. A power distribution energy efficiency management system based on the Internet of Things, based on the power distribution energy efficiency management method based on the Internet of Things according to claim 5, characterized in that: It includes the following modules: A data acquisition module is used to obtain real-time power distribution data, equipment rating data and equipment connection relationships of power distribution equipment in the power distribution area; An equipment classification module is used to compare the real-time distribution data of the distribution equipment with its equipment rated data, and according to the comparison result, classify the distribution equipment in the distribution area into abnormal distribution equipment and non-abnormal distribution equipment, and classify the non-abnormal distribution equipment into a normal load set and an over / under load set; A matrix construction module is used to number the normal load centralized normal load equipment, number the super / low load centralized super / low load equipment, and construct a node matrix according to the connection relationship between the normal load centralized normal load equipment and the super / low load centralized super / low load equipment; An energy efficiency matching degree calculation module is used to select some normal load equipment from the normal load set, select some super / low load equipment from the super / low load set, and calculate the energy efficiency matching degree corresponding to the selected normal load equipment and the selected super / low load equipment; A matching matrix generation module is used to place the corresponding energy efficiency matching degree in the node matrix to generate a matching matrix; The maximum weight calculation module is used to perform the maximum weight allocation operation on the matching matrix through the KM algorithm to obtain the maximum weight matrix; The system optimization module is used to calculate the system fitness of the maximum weight matrix, compare the system fitness of the maximum weight matrix with the fitness threshold, and judge whether the current maximum weight matrix is ​​reliable based on the comparison result. If it is reliable, the maximum weight matrix is ​​output. If it is unreliable, the energy efficiency matching calculation module is used to reselect normal load equipment and over / under load equipment, and recalculate the energy efficiency matching.

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