New energy cluster intelligent regulation and control method and system based on minimum hamming distance analysis

Abstract

The application belongs to the technical field of new energy power generation, and discloses a new energy group intelligent regulation and control method and system based on minimum Hamming distance analysis; the method comprises the following steps: obtaining historical data of a target new energy group, and grouping the historical data; performing binary coding on each group of historical data; obtaining data of the target new energy group at a current time and a power control instruction at a next time; performing binary coding on the data of the target new energy group at the current time, and finding a group where the summarized power is located according to the power control instruction; calculating the Hamming distance between the binary coding of the data of the target new energy group at the current time and the binary coding of the selected historical data, taking the historical data corresponding to the minimum Hamming distance as a control reference, and regulating and controlling the target new energy group according to the control reference. The application can fully utilize historical data, and can quickly control the least group to achieve the purpose of power scheduling adjustment.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation technology, and in particular relates to a method and system for intelligent control of new energy power generation clusters based on minimum Hamming distance analysis. Background Technology

[0002] A renewable energy power station refers to all equipment at the grid connection point of a wind farm or solar power station that is centrally connected to the power system. This equipment may include transformers, lines, busbars, wind turbines, photovoltaic power generation equipment, converters, energy storage, reactive power regulation equipment, and auxiliary equipment. With the continuous construction and commissioning of renewable energy power stations in the power industry, newly commissioned renewable energy power stations are constantly being connected to the grid. As renewable energy power stations continue to be connected to the grid, the ability to quickly and efficiently control the power output of the increasingly large renewable energy fleet has become a crucial technical problem that must be solved in this field. Summary of the Invention

[0003] The purpose of this invention is to propose an intelligent control method and system for new energy vehicle fleets based on minimum Hamming distance analysis, so as to solve the above-mentioned technical problems.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a method for intelligent control of a new energy fleet based on minimum Hamming distance analysis, comprising:

[0006] Historical data of the target new energy vehicle fleet is acquired, and the historical data is grouped to obtain a preset number of groups; each group of historical data is binary encoded.

[0007] The system acquires the current data and the power control command for the next moment of the target new energy generator group; it encodes the current data of the target new energy generator group into binary and finds the group containing the sum of power according to the power control command; it calculates the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and uses the historical data corresponding to the minimum Hamming distance as the control reference, and regulates the target new energy generator group according to the control reference.

[0008] A further improvement of the present invention is that: in the step of obtaining historical data of the target new energy generator group, the historical data includes the power data and total power of all new energy generator groups.

[0009] A further improvement of the present invention is that: in the step of acquiring historical data of the target new energy vehicle group, historical data of 1 year or more is acquired; the sampling interval of historical data is 15 minutes.

[0010] A further improvement of the present invention is that the step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

[0011] A further improvement of this invention is that the step of binary encoding for each set of historical data specifically includes:

[0012] For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels:

[0013] Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance.

[0014] The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable;

[0015] Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity.

[0016] The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable.

[0017] The unit is operating at 80-90% of its output capacity, which is classified as Level 5.

[0018] The unit is operating at 70-80% of its output capacity, which is classified as Level VI.

[0019] The unit is operating at 60-70% of its output capacity, which is classified as level seven.

[0020] The unit is operating at 50-60% of its output capacity, which is classified as level eight.

[0021] The unit is operating at 40-50% of its output capacity, which is classified as level nine.

[0022] The unit is operating at 30-40% of its output capacity, which is classified as level ten.

[0023] The unit is operating at 20-30% of its output capacity, which is classified as level eleven.

[0024] Select a set of historical data that has been divided into eleven levels to begin coding;

[0025] For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code.

[0026] The coding continues, corresponding to the subsequent ten-level division.

[0027] The second-level classification assigns 1 to uncontrollable units, forming a second-level code;

[0028] The three-level classification is 1 for fully powered units, forming the third-level code;

[0029] The fourth-level classification is for low-output units, forming a fourth-level code;

[0030] The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code;

[0031] The sixth level division is based on 70-80% output being 1, forming the sixth level code;

[0032] The seventh level division is based on 60-70% output being 1, forming the seventh level code;

[0033] The eight-level division is based on 50-60% output being 1, forming the eighth-level code;

[0034] The nine-level division is based on 40-50% output being 1, forming the ninth-level code;

[0035] The ten-level division is that 30-40% of the output is 1, forming the tenth-level code;

[0036] The eleventh level division is where 20-30% of the output is 1, forming the eleventh level code;

[0037] The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

[0038] Secondly, the present invention provides an intelligent control device for a new energy fleet based on minimum Hamming distance analysis, comprising:

[0039] The grouping and encoding module is used to acquire historical data of the target new energy vehicle fleet, and to group the historical data to obtain a preset number of groups; and to perform binary encoding on each group of historical data.

[0040] The control module is used to acquire the current data of the target new energy generator group and the power control command for the next moment; encode the current data of the target new energy generator group into binary, and find the group containing the sum of power according to the power control command; calculate the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and use the historical data corresponding to the minimum Hamming distance as the control reference, and control the target new energy generator group according to the control reference.

[0041] A further improvement of this invention is that: the historical data includes the power data and total power of all new energy units; historical data of one year or more is obtained; and the sampling interval for historical data is 15 minutes.

[0042] The step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

[0043] A further improvement of this invention is that the step of binary encoding for each set of historical data specifically includes:

[0044] For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels:

[0045] Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance.

[0046] The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable;

[0047] Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity.

[0048] The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable.

[0049] The unit is operating at 80-90% of its output capacity, which is classified as Level 5.

[0050] The unit is operating at 70-80% of its output capacity, which is classified as Level VI.

[0051] The unit is operating at 60-70% of its output capacity, which is classified as level seven.

[0052] The unit is operating at 50-60% of its output capacity, which is classified as level eight.

[0053] The unit is operating at 40-50% of its output capacity, which is classified as level nine.

[0054] The unit is operating at 30-40% of its output capacity, which is classified as level ten.

[0055] The unit is operating at 20-30% of its output capacity, which is classified as level eleven.

[0056] Select a set of historical data that has been divided into eleven levels to begin coding;

[0057] For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code.

[0058] The coding continues, corresponding to the subsequent ten-level division.

[0059] The second-level classification assigns 1 to uncontrollable units, forming a second-level code;

[0060] The three-level classification is 1 for fully powered units, forming the third-level code;

[0061] The fourth-level classification is for low-output units, forming a fourth-level code;

[0062] The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code;

[0063] The sixth level division is based on 70-80% output being 1, forming the sixth level code;

[0064] The seventh level division is based on 60-70% output being 1, forming the seventh level code;

[0065] The eight-level division is based on 50-60% output being 1, forming the eighth-level code;

[0066] The nine-level division is based on 40-50% output being 1, forming the ninth-level code;

[0067] The ten-level division is that 30-40% of the output is 1, forming the tenth-level code;

[0068] The eleventh level division is where 20-30% of the output is 1, forming the eleventh level code;

[0069] The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

[0070] Thirdly, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis.

[0071] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] This invention discloses an intelligent control method and system for a new energy power generation cluster based on minimum Hamming distance analysis. It utilizes historical data to separately group and binary-encode the output of different units within the cluster at certain intervals. When the new energy power generation cluster receives a power control command, the binary codes of different units within the cluster are correlated with the codes in the historical data. By calculating the minimum Hamming distance, the corresponding historical data is found, and adjustments are made accordingly, achieving efficient and minimal control. This invention can control as few units as possible within the cluster, thereby reducing control time, improving control efficiency, and achieving the goal of power control.

[0074] This invention is based on the analysis of historical power output data of new energy generator groups and has been encoded in binary form. On the one hand, it can make full use of historical data and avoid carrying out time-consuming global optimization; on the other hand, it can quickly achieve the purpose of power scheduling and adjustment by controlling the minimum number of generators. Attached Figure Description

[0075] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0076] Figure 1 A flowchart illustrating the steps for grouping data;

[0077] Figure 2 Here is a flowchart of the coding steps;

[0078] Figure 3 This is a flowchart illustrating a new energy vehicle fleet power adjustment method based on minimum Hamming distance, according to an embodiment of the present invention.

[0079] Figure 4 This is a structural block diagram of a new energy vehicle fleet power adjustment device based on minimum Hamming distance, according to an embodiment of the present invention.

[0080] Figure 5This is a structural block diagram of an electronic device according to the present invention. Detailed Implementation

[0081] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0082] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0083] Hamming distance is used in data transmission error control coding. It's a concept representing the number of different characters at corresponding positions between two words of the same length, denoted by d(x, y). The Hamming distance is calculated by performing an XOR operation on two strings and counting the number of 1s in the result. In information theory, Hamming distance represents the number of different characters at corresponding positions between two strings of equal length, denoted by d(x, y). From another perspective, Hamming distance measures the minimum number of substitutions required to transform string x into string y.

[0084] Hamming distance analysis has applications in fields including information theory, coding theory, and cryptography. For example, in the process of information encoding, to enhance fault tolerance, the minimum Hamming distance between codes should be as large as possible. In information encoding, the number of bits that differ in corresponding positions between two valid codes is called the code distance, or Hamming distance. The number of bits that differ in corresponding bit values ​​between two codewords is called the Hamming distance between the two codewords. For example, 10101 and 00110 differ in the first, fourth, and fifth bits respectively, so the Hamming distance is 3. Minimum Hamming distance: For example, the distance between (00) and (01) is 1, and the distance between (110) and (101) is 2. In a set of code groups, the minimum Hamming distance between any two codes is called the minimum Hamming distance of that code group. The smaller the minimum Hamming distance, the higher the similarity between code groups.

[0085] Therefore, this invention applies Hamming distance to the power regulation of new energy generator groups. By comparing the current output of the generator group with the historical output corresponding to the target to be adjusted, the adjustment of the generator group is minimized when the Hamming distance is minimized.

[0086] Example 1

[0087] This invention provides a power adjustment method for new energy vehicle fleets based on minimum Hamming distance, which is mainly divided into two parts. The first part is how to classify historical data and encode it in binary; the second part is to obtain the adjustment scheme using minimum Hamming distance.

[0088] Please see Figure 1 As shown in the diagram, the first part describes the steps for grouping and binary encoding using the total output of the new energy power plant and the historical data of each unit. Specifically, these steps include:

[0089] (1) For the target new energy generator group, acquire historical data of the target new energy generator group (number of units is M) for a set time. The sampling interval of the historical data is 15 minutes or other preset time. In one specific embodiment, the set time is at least 1 year of historical data. The historical data includes the power data of all new energy generator units and the total power. The total power is the sum of the power data of all new energy generator units in a sampling time.

[0090] (2) Based on the total power output of the new energy generator fleet, the data is grouped into intervals of 10% of the total installed capacity. That is, regardless of the output of the generators, as long as the total power output is consistent (within a 10% range), they are grouped together. All historical data are divided into a total of 10 groups. That is, the first group is 0-10% of the total installed capacity B, where 0≤A<10%; the second group is 10-20% of the total installed capacity B, where 10≤A<20%; the third group is 20-30% of the total installed capacity B, where 20≤A<30%; and the fourth group is 30-40% of the total installed capacity B. Group 5 is where the total installed capacity of power A accounts for 40-50% of the total installed capacity of power B, and group 6 is where the total installed capacity of power A accounts for 50-60% of the total installed capacity of power B, and group 7 is where the total installed capacity of power A accounts for 60-70% of the total installed capacity of power B, and group 8 is where the total installed capacity of power A accounts for 70-80% of the total installed capacity of power B, and group 9 is where the total installed capacity of power A accounts for 80-90% of the total installed capacity of power B, and group 10 is where the total installed capacity of power A accounts for 90-100% of the total installed capacity of power B, and group 10 is where the total installed capacity of power A accounts for 90-100% of the total installed capacity of power B, and group 11 is where the total installed capacity of power A accounts for 30-40% of the total installed capacity of power B, and group 12 is where the total installed capacity of power A accounts for 40-50% of the total installed capacity of power B, and group 13 is where the total installed capacity of power A accounts for 40-50% of the total installed capacity of power B, and group 14 is where the total installed capacity of power A accounts for 40-50% of the total installed capacity of power B, and group 15 is where the total installed capacity of power A accounts for 70-80% of the total installed capacity of power B, and group 14 is where the total installed capacity of power A accounts for 70-80% of the total installed capacity of power B, and group 15 is where the total installed capacity of power A accounts for 80-90% of the total installed capacity of power B, and group 16 is where the total installed capacity of power A accounts for 90-100% of the total installed capacity of power B, and group 17 is where the total installed capacity of power A accounts for 90-100% of the total installed capacity of power B, and

[0091] (3) Select the data from the group after grouping in step (2) (including historical data from N sampling times), and start analyzing the power data of each new energy unit in the selected group. Divide the power data of the new energy units into eleven levels:

[0092] Unit availability is classified as a primary categorization: clearly defining how many units in the target new energy power group are generating electricity and how many units are out of service for maintenance, given a given power output.

[0093] The adjustability of the generating units is classified as a secondary feature: clearly defining how many generating units in the target new energy generating group are adjustable and can generate electricity normally, and how many generating units are not adjustable;

[0094] Whether the generating units are operating at full capacity is categorized into three levels: how many units in the target new energy generating group are operating at full capacity, and how many units are not operating at full capacity.

[0095] The four-level classification is based on the unit's non-adjustable low output (less than 20%): how many units in the target new energy power generation group have non-adjustable low output, and how many units have adjustable output.

[0096] The unit is operating at 80-90% of its output capacity, which is classified as Level 5.

[0097] The unit is operating at 70-80% of its output capacity, which is classified as Level 6.

[0098] The unit is operating at 60-70% of its output capacity, which is classified as level seven.

[0099] The unit is operating at 50-60% of its output capacity, which is classified as level eight.

[0100] The unit is operating at 40-50% of its output capacity, which is classified as level nine.

[0101] The unit is in the 30-40% output range, which is classified as level ten.

[0102] The unit is operating at 20-30% of its output capacity, which is classified as level eleven.

[0103] Check if all power levels have been allocated. If not, continue allocating power. If all power levels have been allocated, select "End".

[0104] Please see Figure 2 As shown, after sorting through the historical data, the next step is to encode each segmented data point. Each historical data point needs a corresponding code, and subsequent adjustments will be made using the minimum Hamming distance to this code. The specific encoding steps include:

[0105] (1) First, select a set of historical data after being divided into eleven levels (including historical data at M sampling times, and the power data of N new energy units in the historical data at each sampling time) to start encoding;

[0106] (2) Secondly, for the historical data at a certain sampling time in the historical data after eleven levels of division, the first level is divided into the availability code of the unit. First, all units are coded according to 0, that is, the number of units is coded as many as the number of 0s. In a specific implementation, if there are N new energy units, a code consisting of N 0s is obtained.

[0107] (3) Then, based on this, according to the first-level division results, check how many unavailable units are in the historical data of the selected sampling time. Starting from the last bit of the code and moving backward, change the 0 in the corresponding code to 1. That is, the number of 1s from the end to the beginning corresponds to the number of unavailable units, thus obtaining the first-level code. In a specific implementation, if 2 out of 5 new energy units are unavailable, the first-level code formed is: 000111.

[0108] (4) Similarly, the coding is carried out for the ten levels that follow.

[0109] a) The second-level classification assigns 1 to uncontrollable units, forming a second-level code;

[0110] b) The three-level division is 1 for fully operational units, forming the third-level code;

[0111] c) The fourth-level classification is for low-output units, forming the fourth-level code 1;

[0112] d) The five-level division is based on 80-90% output being 1, forming the fifth-level code;

[0113] e) The sixth level division is based on 70-80% output being 1, forming the sixth level code;

[0114] f) The seventh level division is 60-70% output as 1, forming the seventh level code;

[0115] g) The eighth-level division is 50-60% output as 1, forming the eighth-level code;

[0116] h) The nine-level division is that 40-50% of the output is 1, forming the ninth-level code;

[0117] i) The ten-level division is that 30-40% of the output is 1, forming the tenth-level code;

[0118] j) The eleventh level division is 20-30% output as 1, forming the eleventh level code.

[0119] (5) According to the eleven levels, the first level code is the highest bit, and the remaining bits are successively reduced and merged into a final binary code.

[0120] (6) Check if all data is encoded. If not, select a new data and start from step (2).

[0121] Part Two mainly includes the following steps:

[0122] After the encoding steps in the previous two sections, each piece of power data collected from the generator group can be assigned a binary code. This way, once the generator group receives a power control command, it can directly access the aggregated power data for the power to be controlled, find the corresponding generator group data, and then use Hamming distance to calculate the smallest Hamming distance between the data and its corresponding binary code. This distance is then used as the initial value for control of the target renewable energy generator group's output.

[0123] If the current generator group's binary code is 00111, and its total power accounts for 35% of the total installed capacity, and the power control command indicates that the total power of the generators needs to be controlled to reach 45% of the total installed capacity, then in the fifth group of historical data (the fifth group is defined as total power A accounting for 40-50% of total installed capacity B, where 40≤A<50%), the Hamming distance between the current generator group's binary code 00111 and all other binary codes is calculated. For example, if there is a historical data point in the fifth group with a binary code of 00110, the Hamming distance value is 1, which is the minimum Hamming distance. Therefore, 00110 is selected as the control reference, thus adjusting the minimum amount. This approach does not restrict adjustments to specific individual generators but rather adjusts what can be adjusted within a class of generators within a given output range. Previous optimization algorithms optimized each individual unit, while the method proposed in this invention optimizes according to category and quantity. For example, if 10 units with 30-40% output need to be adjusted, 5 units can be reduced to 20-30%, instead of specifying which five units, thus avoiding complex algorithms.

[0124] Example 2

[0125] Please see Figure 3 As shown, this invention provides a method for intelligent control of a new energy vehicle fleet based on minimum Hamming distance analysis, comprising:

[0126] S1. Obtain historical data of the target new energy vehicle fleet, and group the historical data to obtain a preset number of groups; encode each group of historical data into binary format;

[0127] S2. Obtain the current data of the target new energy generator group and the power control command for the next moment; encode the current data of the target new energy generator group into binary, and find the group containing the sum of power according to the power control command; calculate the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary code of all historical data in the selected group, and use the historical data corresponding to the minimum Hamming distance as the control reference, and regulate the target new energy generator group according to the control reference.

[0128] In one specific implementation: in the step of acquiring historical data of the target new energy generator group, the historical data includes the power data and total power of all new energy generator groups.

[0129] In one specific implementation: in the step of acquiring historical data of the target new energy vehicle fleet, historical data of 1 year or more is acquired; the sampling interval of historical data is 15 minutes.

[0130] In one specific implementation: the step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

[0131] A further improvement of this invention is that the step of binary encoding for each set of historical data specifically includes:

[0132] For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels:

[0133] Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance.

[0134] The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable;

[0135] Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity.

[0136] The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable.

[0137] The unit is operating at 80-90% of its output capacity, which is classified as Level 5.

[0138] The unit is operating at 70-80% of its output capacity, which is classified as Level VI.

[0139] The unit is operating at 60-70% of its output capacity, which is classified as level seven.

[0140] The unit is operating at 50-60% of its output capacity, which is classified as level eight.

[0141] The unit is operating at 40-50% of its output capacity, which is classified as level nine.

[0142] The unit is operating at 30-40% of its output capacity, which is classified as level ten.

[0143] The unit is operating at 20-30% of its output capacity, which is classified as level eleven.

[0144] Select a set of historical data that has been divided into eleven levels to begin coding;

[0145] For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code.

[0146] The coding continues, corresponding to the subsequent ten-level division.

[0147] The second-level classification assigns 1 to uncontrollable units, forming a second-level code;

[0148] The three-level classification is 1 for fully powered units, forming the third-level code;

[0149] The fourth-level classification is for low-output units, forming a fourth-level code;

[0150] The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code;

[0151] The sixth level division is based on 70-80% output being 1, forming the sixth level code;

[0152] The seventh level division is based on 60-70% output being 1, forming the seventh level code;

[0153] The eight-level division is based on 50-60% output being 1, forming the eighth-level code;

[0154] The nine-level division is based on 40-50% output being 1, forming the ninth-level code;

[0155] The ten-level division is that 30-40% of the output is 1, forming the tenth-level code;

[0156] The eleventh level division is where 20-30% of the output is 1, forming the eleventh level code;

[0157] The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

[0158] Example 3

[0159] Please see Figure 4 As shown, this invention provides an intelligent control device for a new energy fleet based on minimum Hamming distance analysis, comprising:

[0160] The grouping and encoding module is used to acquire historical data of the target new energy vehicle fleet, and to group the historical data to obtain a preset number of groups; and to perform binary encoding on each group of historical data.

[0161] The control module is used to acquire the current data of the target new energy generator group and the power control command for the next moment; encode the current data of the target new energy generator group into binary, and find the group containing the sum of power according to the power control command; calculate the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and use the historical data corresponding to the minimum Hamming distance as the control reference, and control the target new energy generator group according to the control reference.

[0162] In one specific implementation: the historical data includes the power data and total power of all new energy units; historical data of one year or more is acquired; the historical data sampling interval is 15 minutes;

[0163] The step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

[0164] In one specific implementation: the step of binary encoding for each set of historical data specifically includes:

[0165] For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels:

[0166] Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance.

[0167] The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable;

[0168] Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity.

[0169] The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable.

[0170] The unit is operating at 80-90% of its output capacity, which is classified as Level 5.

[0171] The unit is operating at 70-80% of its output capacity, which is classified as Level VI.

[0172] The unit is operating at 60-70% of its output capacity, which is classified as level seven.

[0173] The unit is operating at 50-60% of its output capacity, which is classified as level eight.

[0174] The unit is operating at 40-50% of its output capacity, which is classified as level nine.

[0175] The unit is operating at 30-40% of its output capacity, which is classified as level ten.

[0176] The unit is operating at 20-30% of its output capacity, which is classified as level eleven.

[0177] Select a set of historical data that has been divided into eleven levels to begin coding;

[0178] For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code.

[0179] The coding continues, corresponding to the subsequent ten-level division.

[0180] The second-level classification assigns 1 to uncontrollable units, forming a second-level code;

[0181] The three-level classification is 1 for fully powered units, forming the third-level code;

[0182] The fourth-level classification is for low-output units, forming a fourth-level code;

[0183] The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code;

[0184] The sixth level division is based on 70-80% output being 1, forming the sixth level code;

[0185] The seventh level division is based on 60-70% output being 1, forming the seventh level code;

[0186] The eight-level division is based on 50-60% output being 1, forming the eighth-level code;

[0187] The nine-level division is based on 40-50% output being 1, forming the ninth-level code;

[0188] The ten-level division is that 30-40% of the output is 1, forming the tenth-level code;

[0189] The eleventh level division is where 20-30% of the output is 1, forming the eleventh level code;

[0190] The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

[0191] Example 4

[0192] Please see Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a method for intelligent control of a new energy fleet based on minimum Hamming distance analysis; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0193] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the intelligent control method for new energy vehicle fleet based on minimum Hamming distance analysis described in Embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0194] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0195] The memory 101 in the electronic device 100 stores multiple instructions to implement a new energy vehicle fleet intelligent control method based on minimum Hamming distance analysis, and the processor 102 can execute the multiple instructions to achieve the following:

[0196] Historical data of the target new energy vehicle fleet is acquired, and the historical data is grouped to obtain a preset number of groups; each group of historical data is binary encoded.

[0197] The system acquires the current data and the power control command for the next moment of the target new energy generator group; it encodes the current data of the target new energy generator group into binary and finds the group containing the sum of power according to the power control command; it calculates the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and uses the historical data corresponding to the minimum Hamming distance as the control reference, and regulates the target new energy generator group according to the control reference.

[0198] Example 5

[0199] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0200] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0202] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent control of new energy vehicle fleets based on minimum Hamming distance analysis, characterized in that, include: Acquire historical data of the target new energy vehicle fleet, and group the historical data based on the total power output to obtain a preset number of groups; Each set of historical data is binary encoded; The system acquires the current data and the power control command for the next moment of the target new energy generator group; it encodes the current data of the target new energy generator group into binary and finds the group containing the total power according to the power control command; it calculates the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and uses the historical data corresponding to the minimum Hamming distance as the control reference, and regulates the target new energy generator group according to the control reference.

2. The intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis according to claim 1, characterized in that, In the step of obtaining historical data of the target new energy generator group, the historical data includes the power data and total power of all new energy generator groups.

3. The intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis according to claim 1 or 2, characterized in that, In the step of obtaining historical data of the target new energy vehicle fleet, historical data of 1 year or more is obtained; the sampling interval of historical data is 15 minutes.

4. The intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis according to claim 1, characterized in that, The step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

5. The intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis according to claim 4, characterized in that, The step of binary encoding for each set of historical data specifically includes: For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels: Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance. The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable; Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity. The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable. The unit is operating at 80-90% of its output capacity, which is classified as Level 5. The unit is operating at 70-80% of its output capacity, which is classified as Level VI. The unit is operating at 60-70% of its output capacity, which is classified as level seven. The unit is operating at 50-60% of its output capacity, which is classified as level eight. The unit is operating at 40-50% of its output capacity, which is classified as level nine. The unit is operating at 30-40% of its output capacity, which is classified as level ten. The unit is operating at 20-30% of its output capacity, which is classified as Level 11. Select a set of historical data that has been divided into eleven levels to begin coding; For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code. The coding continues, corresponding to the subsequent ten-level division. The second-level classification assigns 1 to uncontrollable units, forming a second-level code; The three-level classification is 1 for fully powered units, forming the third-level code; The fourth-level classification is for low-output units, forming a fourth-level code; The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code; The sixth level division is based on 70-80% output being 1, forming the sixth level code; The seventh level division is based on 60-70% output being 1, forming the seventh level code; The eight-level division is based on 50-60% output being assigned a value of 1, forming the eighth-level code; The nine-level division is based on 40-50% output being 1, forming the ninth-level code; The ten-level division is that 30-40% of the output is 1, forming the tenth-level code; The eleventh level division is where 20-30% of the output is set to 1, forming the eleventh level code; The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

6. A new energy fleet intelligent control device based on minimum Hamming distance analysis, characterized in that, include: The grouping and coding module is used to acquire historical data of the target new energy vehicle group and group the historical data based on the total power size to obtain a preset number of groups; Each set of historical data is binary encoded; The control module is used to acquire the current data of the target new energy generator group and the power control command for the next moment; encode the current data of the target new energy generator group into binary, and find the group containing the total power according to the power control command; calculate the Hamming distance between the binary code of the target new energy generator group data at the current moment and the binary codes of all historical data in the selected group, and use the historical data corresponding to the minimum Hamming distance as the control reference to control the target new energy generator group.

7. The intelligent control device for new energy fleets based on minimum Hamming distance analysis according to claim 6, characterized in that, The historical data includes power data and aggregated power of all new energy units; historical data of 1 year or more is obtained; the historical data sampling interval is 15 minutes. The step of grouping the historical data to obtain a preset number of groups specifically includes: dividing all historical data into 10 groups based on the percentage of the total power as a percentage of the total installed capacity of the target new energy vehicle group from 0% to 100%, with each 10% interval.

8. The intelligent control device for new energy fleets based on minimum Hamming distance analysis according to claim 6, characterized in that, The step of binary encoding for each set of historical data specifically includes: For all data in all groups, analyze the power data of each new energy unit, and divide the power data of the new energy units into eleven levels: Unit availability is classified as a primary categorization: under a given power output, how many units in the target new energy power group are generating electricity, and how many units are shut down for maintenance. The adjustability of the generating units is classified as a secondary category: in the target new energy generating group, how many units are adjustable and can generate electricity normally, and how many units are not adjustable; Whether the generating units are operating at full capacity is divided into three levels: how many units in the target new energy generating group are operating at full capacity and how many units are not operating at full capacity. The units are classified into four levels based on their non-adjustable low output: how many units in the target new energy power generation group are at low output and non-adjustable, and how many units are adjustable. The unit is operating at 80-90% of its output capacity, which is classified as Level 5. The unit is operating at 70-80% of its output capacity, which is classified as Level VI. The unit is operating at 60-70% of its output capacity, which is classified as level seven. The unit is operating at 50-60% of its output capacity, which is classified as level eight. The unit is operating at 40-50% of its output capacity, which is classified as level nine. The unit is operating at 30-40% of its output capacity, which is classified as level ten. The unit is operating at 20-30% of its output capacity, which is classified as Level 11. Select a set of historical data that has been divided into eleven levels to begin coding; For the historical data at a certain sampling time in the historical data after eleven levels of division, the first level of division is the availability code of the unit. First, all units are coded as 0. According to the first level division result, check how many units are unavailable in the historical data at the selected sampling time. Starting from the last bit of the code, change the 0 of the corresponding code to 1. The number of unavailable units is the number of 1s from the end to the beginning, thus obtaining the first level code. The coding continues, corresponding to the subsequent ten-level division. The second-level classification assigns 1 to uncontrollable units, forming a second-level code; The three-level classification is 1 for fully powered units, forming the third-level code; The fourth-level classification is for low-output units, forming a fourth-level code; The five-level division is based on 80-90% output being assigned a value of 1, forming the fifth-level code; The sixth level division is based on 70-80% output being 1, forming the sixth level code; The seventh level division is based on 60-70% output being 1, forming the seventh level code; The eight-level division is based on 50-60% output being assigned a value of 1, forming the eighth-level code; The nine-level division is based on 40-50% output being 1, forming the ninth-level code; The ten-level division is that 30-40% of the output is 1, forming the tenth-level code; The eleventh level division is where 20-30% of the output is set to 1, forming the eleventh level code; The system is divided into eleven levels. The highest bit of the first-level code is used, and the remaining bits are sequentially reduced in size and then merged into a final binary code.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the intelligent control method for new energy vehicle fleet based on minimum Hamming distance analysis as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the intelligent control method for new energy vehicle fleets based on minimum Hamming distance analysis as described in any one of claims 1 to 5.

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