CN111815023A - Campus electric heating soft start method based on improved wolf optimization algorithm - Google Patents
Campus electric heating soft start method based on improved wolf optimization algorithm Download PDFInfo
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Abstract
The invention discloses a campus electric heating soft start method based on an improved wolf optimization algorithm, which comprises the following steps: step 1: classifying various rooms in the campus according to grades; step 2: dividing the load input power corresponding to each classification level into a plurality of power inputs; and step 3: acquiring the relationship between temperature rise data and heating power in unit time; and 4, step 4: establishing a moderate function model; and 5: and searching the model through an improved grey wolf optimization algorithm, outputting the optimal power input of each classification level, and performing power control of electric heating on rooms of different classification levels according to power. The starting mode of campus electric heating is improved, the input power is optimized, and the problem of transformer capacity increase in the starting process is avoided. The input power scheduling is optimized by actually measuring the temperature rise of the campus electric heating distributed control system so as to reduce the starting current and save the cost of transformer capacity increase.
Description
Technical Field
The invention relates to the field of group intelligent optimization algorithm and electric heating soft start, in particular to an electric heating soft start control strategy based on an improved wolf optimization algorithm.
Background
The campus in northeast and north China is cold and dry, in order to improve the indoor temperature environment, central heating is generally adopted in winter, the electric heating technology enters a full-automatic control period since the early 70 th century abroad, the research on electric heating in China starts late, but the theoretical research is not lagged behind. Generally, China reaches the leading level in the aspects of electric heating materials, electric heaters and heat storage technologies, but the control technology and the popularization range degree are limited, in northern areas of China, partial campuses are provided with electric heating distributed control systems, and the traditional heating mode is replaced by the electric heating distributed control systems with the purposes of energy conservation and emission reduction. However, for the heating of campus buildings, the functional differences of each heating room, the service time is not consistent, the required heating time and temperature are also different, for example, the heating requirements of office buildings, auditoriums, laboratories, classrooms, libraries and the like are different, some heating equipment does not need 24 hours for heating, the traditional heating equipment does not have an automatic temperature regulating device, and the electric heating is used as a heating mode for providing energy by using electric power, and the problem of no capacity increase of a transformer needs to be considered in the starting process, so that the starting current needs to be avoided from being too large during the starting process, in order to achieve the optimal power in the starting process, and simultaneously consider the soft starting characteristic of graded temperature rise, the text aims at a campus electric heating distributed control system to search a new soft starting strategy, seek balance among various influence factors, and design a proper fitness function by combining the influence factors, and carrying out iterative optimization by using an improved wolf optimization algorithm.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a campus electric heating soft start method based on an improved Grey wolf optimization algorithm, improves the campus electric heating start mode, optimizes input power and avoids the problem of transformer capacity increase in the start process. The input power scheduling is optimized by actually measuring the temperature rise of the campus electric heating distributed control system so as to reduce the starting current and save the cost of transformer capacity increase.
In order to achieve the purpose, the invention adopts the technical scheme that: a campus electric heating soft start method based on an improved wolf optimization algorithm comprises the following steps:
step 1: classifying various rooms in the campus according to grades;
step 2: dividing the load input power corresponding to each classification level into a plurality of power inputs;
and step 3: acquiring the relationship between temperature rise data and heating power in unit time;
and 4, step 4: establishing a moderate function model;
and 5: and searching the model through an improved grey wolf optimization algorithm, outputting the optimal power input of each classification level, and performing power control of electric heating on rooms of different classification levels according to power.
In step 1, the load of rooms is classified into 5 types according to the demand and supply requirements of different rooms and the importance degree, and the most important room is marked as a level 1 load room.
In step 2, the input power u of the class 1-5 load is measured1-u5The load per stage is divided into 10 inputs, i.e. u1＝[u10u11u12… u19]、…u5＝[u50u51u52… u59]。
In step 3, the actual temperature change conditions of each room under different input powers are obtained through experiments, and the experimental data are processed through interpolation modeling.
The model for the fitness function is defined as:
F＝∫K1Δ1+K2Δ2+K3Δ3K4Δ4+K5Δ5+K(Δu1+Δu2+Δu3+Δu4+Δu5)dt
in the formula,. DELTA.1-Δ5The difference between the actual temperature and the expected temperature of the room with different load grades at the time t is respectively; k1-K5The weight coefficients of different levels are the larger the coefficient is, the greater the importance is in the temperature rising process, the faster the temperature rising is, the more energy is consumed, wherein K and K1-K5Setting the numerical value of different weight grades; Δ u1-Δu5Instantaneous values of input power of load rooms of 1-5 classes respectively, representing consumed energy; Δ u1+Δu2+Δu3+Δu4+Δu5Is the instantaneous total power.
The improved grey wolf algorithm is an improved grey wolf optimization algorithm adopting a nonlinear convergence factor and a reverse learning strategy.
The invention provides a soft start control scheme suitable for campus electric heating, and the soft start control scheme is used for describing and classifying various room characteristics in a campus so as to take demand unification, user comfort and grading energy conservation as optimization targets, and simultaneously considers the size of total load, and establishes a campus electric heating soft start fitness function model with grading temperature rise and capacity increase free; the effectiveness of the improved wolf optimization algorithm provided by the invention is verified by the model simulation.
The invention has the technical effects that: (1) according to the supply and demand requirements and the importance degree of different rooms, the room loads are divided into 5 types, the most important room is marked as a level 1 load room, and the rest is done until the level 5 load room is reached, so that the effect of graded temperature rise is achieved. (2) And sampling once in one minute according to the temperature rise of equipment under different powers during operation, recording a temperature rise data table of the highest temperature value from the room temperature to the comfort level of the user, and providing data for the fitness function model. (3) The gray wolf algorithm is improved in a targeted mode, a nonlinear convergence factor is adopted, the optimization speed of the algorithm is improved, and a reverse learning strategy is adopted to avoid a local optimal result. The model is solved as expected and a set of input powers best suited for soft start is provided.
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The contents of the expressions in the various figures of the present specification and the labels in the figures are briefly described as follows:
FIG. 1 is a flow chart of a campus electric heating soft start scheme;
FIG. 2 is a fitness function model;
FIG. 3 is ΔiThe Simulink model of (1);
FIG. 4 is a simulation of an example.
Wherein the curves in fig. 4 represent the temperature rise curves of class 1, 2, 3, 4, 5 load rooms, respectively.
Detailed Description
The following description of preferred embodiments of the invention will be made in further detail with reference to the accompanying drawings.
A campus electric heating soft start control strategy based on an improved Grey wolf optimization algorithm; the method comprises the steps of providing a soft start control scheme suitable for campus electric heating, describing and classifying characteristics of various rooms in a campus to achieve the optimization goals of unification of supply and demand, user comfort and grading energy conservation, considering the size of total load, and establishing a campus electric heating soft start fitness function model with grading temperature rise and capacity increase free; the effectiveness of the improved wolf optimization algorithm provided by the invention is verified by the model simulation. 2. The invention has the technical effects that: according to different loads required by different rooms and different comfort levels of users to temperature, the load of the rooms is divided into 5 types by integrating a plurality of factors such as the importance degree of the rooms and the user quantity, the supply and demand consideration is detailed and comprehensive, and various influence factors are integrated; applying the idea of graded heating to campus electric heating, and building a campus electric heating fitness function model aiming at the problems of room importance, user comfort and transformer stability; the basic gray wolf optimization algorithm is improved, a nonlinear convergence factor is adopted to replace the original convergence factor, the global search capability in the early stage of the algorithm is improved, and the local mining capability in the later stage is increased; an improved reverse learning strategy is adopted, so that the algorithm is not easy to fall into local optimization. 3. The solution to the model is expected and provides a set of optimal input powers. 4. The improved graying algorithm-based campus electric heating soft start control strategy provided by claim 1 comprises the following specific implementation steps: 1. the method comprises the steps of classifying the load of campus rooms, wherein the rooms and user types in the campus are various, and the loads of different types of users and rooms are different; according to the working habits and load requirements of users, the rooms are roughly divided into a leader office type, a classroom type, a reading room type, a gymnasium type and a warehouse type according to the importance from high to low; 2. and recording temperature rise data, sampling once in one minute according to the temperature rise of equipment under different powers during operation, and recording a temperature rise data table of the highest temperature value from the room temperature to the comfort level of a user. 3. Establishing a fitness function model, taking supply and demand unification, user comfort and grading energy conservation as optimization targets, considering the size of total load, and establishing a grading temperature rise and capacity increase free campus electric heating soft start fitness function model; 4. the improvement of the algorithm, adopt nonlinear convergence factor and reversal learning tactics to improve the grey wolf optimization algorithm; 5. and (3) simulating the model, namely verifying the effectiveness of the model through the simulation of the example.
The invention mainly aims to provide a temperature control strategy which is used for conducting simultaneous temperature rise and differential temperature control under the guidance of importance and comprehensively considering total energy consumption in a multi-target scene aiming at the current domestic campus electric heating system. The method can seek balance between energy consumption and temperature rise according to the relative importance among the temperature rise effect of the key area, the temperature rise effect of the non-key area and the total energy consumption. The heating system can realize optimal energy consumption under the premise of ensuring different temperature rise targets at different times in different areas of the school. The specific method of the invention is as follows:
1: and classifying according to importance levels. The discontinuous working area is divided into 5 loads, the conference room and the leader office are 1-level loads, the classroom is 2-level loads, and so on, the loads represent the importance degree, the 1-level is the most important room, the 2-level is the secondary room, and so on. The discontinuous working time is set to be 8.00-17.00.
2: improvement of input power. Input power u of 1-5-stage load1-u5Each stage has 10 inputs, i.e. u1＝[u10u11u12… u19]、…u5＝[u50u51u52… u59]The function of the temperature controller is to control the temperature at different stages of the same input, and the precision of the control system is greatly improved.
3: and acquiring the relationship between the temperature rise data in unit time and the heating power. Through experimental analysis, the temperature rise in unit time is related to the initial temperature and the heating power. In order to obtain the temperature rise data in the required temperature interval and power interval in unit time, besides multiple experiments, interpolation modeling processing needs to be carried out on the experimental data. The specific steps for acquiring the temperature rise data are as follows:
(1) and (6) measuring the temperature in the experiment. The main purpose of this step is to obtain the actual temperature variation of each room at different input powers. The method comprises the following steps:
setting input power to 1000W;
secondly, keeping the input power unchanged in the whole heating process, and recording the current temperature value once every minute from 5 ℃ until the temperature reaches 25 ℃ of saturation;
calculating the temperature rise in unit time under the current power;
fourthly, increasing the heating power by 400W;
fifthly, repeating the processes from (a) to (b) until the power reaches the upper limit of 2200W.
(2) And (6) interpolation processing. To make the control of power finer, extensive extensions of the experimental data obtained in 3.1 are required. And (3) interpolating the data in the table by a mathematical method to obtain a temperature rise table per unit time with 1W as a power minimum change unit. The method comprises the following steps:
recording temperature values every one minute at 1000W, 1400W, 1800W and 2200W power into a single table, wherein rows represent time and columns represent power;
secondly, the following operations are respectively carried out on the 4 independent tables: the row change time is the temperature at this moment, and the table content is the temperature rise within one minute;
interpolation processing is carried out on the 4 tables respectively, 2000 values are inserted between 5 ℃ and 25 ℃, and 100 values are inserted between every two degrees centigrade;
and 4 tables are combined, 1200 values are inserted between 1000W and 2200W, and the conversion unit is 1W. And making a temperature rise table.
4: and (4) designing a fitness function. In order to ensure the uniformity of heat supply and demand and reduce energy consumption, an fitness function combining two characteristics is defined as follows:
in the formula (1) < delta >1-Δ5The difference between the actual temperature and the expected temperature of the room with different load grades at the time t is respectively; k1-K5The weight coefficients of different levels are the larger the coefficient is, the greater the importance is in the temperature rising process, the faster the temperature rising is, the more energy is consumed, wherein K and K1-K5Setting the numerical value of different weight grades; Δ u1-Δu5Instantaneous values of input power of load rooms of 1-5 classes respectively, representing consumed energy; Δ u1+Δu2+Δu3+Δu4+Δu5Is the instantaneous total power.
And 5: GWO improvement of the algorithm.
The wolf species has a very strict level system and can be divided into the following wolfs in the whole hunting team: the first leading wolf, which can also be marked as alpha wolf, is at the first level and is the first brain of the whole wolf group in the hunting process; the second leading wolf species in the second level is beta wolf, which is used to assist alpha wolf to complete the enclosure, the third important leading wolf group in the third level is marked as wolf, which listens to the wolf groups in the first two levels and commands the bottom wolf group theta. Generally, there is only one wolf in each of the first three levels, and in the optimization algorithm, α, β, wolf first estimate the prey position, and get close to the prey, θ wolf group follows the first three leading wolfs to perform the surrounding catching and killing of the prey, thereby completing the whole hunting process.
The gray wolf needs to be searched before locking and approaching the target prey, and the searching prey is described in detail as follows:
D＝|C·XPi(t)-X(t)| (2)
X(t+1)＝XPi(t)-H·D (3)
wherein t represents the number of iterations; 1, 2, 3, …, n, n is the dimension; xPi(t) a prey position representing a current number of iterations; x (t) represents the position of the current wolf pack; h and C are control parameters and rocking factors, defined as follows:
H＝a(2ρ1-1) (4)
C＝2ρ2(5)
where ρ is1、ρ2Are all [0,1]A random variable of (a); a is a convergence factor defined as follows:
a＝2(1-t/tmax) (6)
wherein t ismaxIs the maximum number of iterations. a decreases linearly to 0 with 2 as the number of iterations increases.
The sirius in nature will go around catching and killing the prey after the prey location is determined. However, in the gray wolf optimization algorithm, the prey (i.e. the optimal value) is uncertain, the position of which is mainly explored by α, β, the three wolfs closest to the prey are selected as the best solution after each update iteration, and other wolfs are guided to approach the prey, and the dynamic mathematical model for capturing the prey in the wolf colony is as follows:
Dα＝|C1·Xα(t)-X(t)| (7)
Dβ＝|C2·Xβ(t)-X(t)| (8)
D＝|C3·X(t)-X(t)| (9)
X1＝Xα(t)-H1·Dα(10 )
X2＝Xβ(t)-H2·Dβ(11 )
X3＝X(t)-H3·D(12 )
X(t+1)＝(X1+X2+X3)/3 (13 )
wherein XαIs the location of the optimal solution; xβA position that is suboptimal; xThe positions of the third optimal solution respectively represent alpha, beta and three-headed wolf; ci、Hi(i ═ 1, 2, 3) are randomly generated coefficients; after the head wolf is renewedBy X in positioniAnd (i is 1, 2, 3).
GWO, and an improved gray wolf optimization algorithm is proposed for the two shortcomings.
In the GWO algorithm, when | H | > 1, the wolf pack carries out global search, and the exploration range is enlarged and the global property is enhanced; when | H | < 1, the wolf colony carries out local search, and performs surrounding capture after determining the approximate position of a prey, so that the search range is narrowed, and the efficiency is improved. From the formula (6), a is linearly decreased in the whole process of global search and local search in two important stages of wolf pack search, and the ideal target is that the decrease of a in the early stage is slow to increase the global search capability and expand the search range as much as possible; and the later stage a is in a rapid descending trend, so that the prey can be quickly captured, and the local search efficiency is improved. Therefore, a should be a convex function, and based on this, an exponential function is introduced to change the convergence, and the specific formula is as follows:
wherein a isfinTaking 2 as the final value of the parameter; a isiniTaking 0 as the initial value of the parameter; and t is the current iteration number.
At the initial stage of GWO algorithm, the wolf pack can be randomly distributed in the search space in diversity, the diversity of the wolf pack is better at this time, and the global search capability of the algorithm can be improved. With the continuous increase of iteration, theta continuously approaches to an optimal solution area guided by a leading wolf, and if alpha is a local optimal solution, the iteration is easy to fall into the local optimal solution at last, because all wolf groups are searched in a narrow area after the iteration is finished in the later period, the diversity is reduced, and a reverse learning concept is introduced for enhancing the later-period global search capability.
The inverse learning (OBL) has been proved to be an efficient method for improving the random search capability as a new concept in computational intelligence, and the reasonable utilization of the inverse learning can help the diversity improvement of the algorithm in the iterative process, so that the algorithm can jump out of the local optimal solution. The definition is as follows:
defining 1 a one-dimensional space, a real number x in the interval [ a, b ], and its inverse point x' can be defined as:
x′＝a+b-x (15)
for vectors in a high-dimensional space, the following is defined:
xi′＝ai+bi-xi(16)
it can be understood that equation (15) is that the real number x and its inverse number x ' in an interval are symmetric about the center of the interval, and the inverse number at this time is only one ' symmetric number ' more than the original one, and in the iterative process of the gray wolf optimization algorithm, the inverse learning only increases the diversity and cannot effectively increase the global property of the algorithm, so the improvement is as follows:
xi′＝ai+bi-α+(α-xi)·z (17)
g＝a-2 (18)
the fitness value of alpha is introduced into the formula (17), wherein z is a random number uniformly distributed in an interval [0, 1], the improved reverse vector is guided by the fitness value of the alpha wolf closest to the optimal solution, meanwhile, the randomness, the diversity and the globality are increased, the dimension is recorded as dim, and the specific implementation steps are as follows:
1: judging the sizes of z and g, if the former is larger than the latter, calculating an expression (17), wherein the purpose of the larger size is to perform reverse learning from the beginning of the algorithm and increase the diversity;
2: merging the position of the fitness value and the reverse value at the moment to generate a 2dim multiplied by dim matrix, calculating the fitness value of each row to generate a 1 multiplied by 2dim fitness value matrix;
3: and arranging the fitness values in the generated fitness value matrix from small to large in an ascending order to form a new matrix, and replacing the original position information with the positions of the first dim high-quality fitness values.
And 5: and (6) solving the model. The solving process is as follows:
1: initializing wolf group parameters including wolf group N, maximum iteration number tmaxDimension dim, upper and lower limits ub and lb of the search space, and weighting coefficients K and K1-K5The desired temperature Tref.
2: updating a convergence factor alpha according to an equation (14), calculating the individual fitness value of the wolf group according to an electric heating fitness function equation (1) and determining alpha, beta and beta.
3: the parameters H, C are updated by equations (4), (5).
4: updating the wolf group position according to (7) to (12), and updating the prey position by the formula (13).
5: and calculating the fitness value for reverse learning, calculating a reverse number through an equation (17), combining and comparing, and screening out a new fitness value in an ascending order.
6: jump to step 2 until a termination condition is met, i.e. calculate to maximum number of iterations tmax。
7: output u1-u5。
Step 6: example simulation. The experimental object is to select an electric heating DCS distributed control system of 30 ten thousand square meters in a college and universities of Changchun, and carry out temperature measurement, interpolation processing and mathematical modeling according to the experimental steps. Setting parameters: the number of wolf clusters is 50, the maximum iteration number is 30, the dimensionality is 50, and K1-K5100000, 60000, 20000, 8000, 4000, K1, and Tref 25 are expected temperatures for 5 class load rooms. The fitness function model is shown in fig. 2 and fig. 3.
As can be seen from fig. 4, the load rooms of class 1 and class 2 are kept at the highest temperature and priority level throughout the whole temperature rise process, while the load rooms of class 3, 4 and 5 are at the same temperature in 9 minutes, and the priority level achieves the ideal effect in the subsequent temperature rise process.
It is clear that the specific implementation of the invention is not restricted to the above-described embodiments, but that various insubstantial modifications of the inventive process concept and technical solutions are within the scope of protection of the invention.
Claims (6)
1. A campus electric heating soft start method based on an improved Grey wolf optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1: classifying various rooms in the campus according to grades;
step 2: dividing the load input power corresponding to each classification level into a plurality of power inputs;
and step 3: acquiring the relationship between temperature rise data and heating power in unit time;
and 4, step 4: establishing a moderate function model;
and 5: and searching the model through an improved grey wolf optimization algorithm, outputting the optimal power input of each classification level, and performing power control of electric heating on rooms of different classification levels according to power.
2. The improved grayish wolf optimization algorithm-based campus electric heating soft start method as claimed in claim 1, characterized in that: in step 1, the load of rooms is classified into 5 types according to the demand and supply requirements of different rooms and the importance degree, and the most important room is marked as a level 1 load room.
3. The improved grayish wolf optimization algorithm-based campus electric heating soft start method as claimed in claim 1, characterized in that: in step 2, the input power u of the class 1-5 load is measured1-u5The load per stage is divided into 10 inputs, i.e. u1＝[u10u11u12…u19]、…u5＝[u50u51u52…u59]。
4. The improved grayish wolf optimization algorithm-based campus electric heating soft start method as claimed in claim 1, characterized in that: in step 3, the actual temperature change conditions of each room under different input powers are obtained through experiments, and the experimental data are processed through interpolation modeling.
5. The improved grayish wolf optimization algorithm-based campus electric heating soft start method as claimed in claim 1, characterized in that: the model for the fitness function is defined as:
in the formula (1) < delta >1-Δ5The difference between the actual temperature and the expected temperature of the room with different load grades at the time t is respectively; k1-K5The weight coefficients of different levels are the larger the coefficient is, the greater the importance is in the temperature rising process, the faster the temperature rising is, the more energy is consumed, wherein K and K1-K5Setting the numerical value of different weight grades; Δ u1-Δu5Instantaneous values of input power of load rooms of 1-5 classes respectively, representing consumed energy; Δ u1+Δu2+Δu3+Δu4+Δu5Is the instantaneous total power.
6. The improved grayish wolf optimization algorithm-based campus electric heating soft start method as claimed in claim 1, characterized in that: the improved grey wolf algorithm is an improved grey wolf optimization algorithm adopting a nonlinear convergence factor and a reverse learning strategy.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114992808A (en) * | 2022-06-15 | 2022-09-02 | 山东大学 | Heat pump air conditioner heat management control method and system based on combined intelligent algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909141A (en) * | 2017-11-27 | 2018-04-13 | 温州大学 | A kind of data analysing method and device based on grey wolf optimization algorithm |
| CN109737491A (en) * | 2019-01-10 | 2019-05-10 | 吉林建筑大学城建学院 | A kind of given value optimization method in campus electric heating DCS control system |
| CN109861297A (en) * | 2019-04-11 | 2019-06-07 | 上海电机学院 | A kind of power system blackstart method based on grey wolf optimization algorithm |
| CN110729939A (en) * | 2019-11-08 | 2020-01-24 | 江苏科技大学 | Parameter setting method for rotating speed ring active disturbance rejection controller of permanent magnet synchronous motor |
-
2020
- 2020-06-05 CN CN202010505147.1A patent/CN111815023A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107909141A (en) * | 2017-11-27 | 2018-04-13 | 温州大学 | A kind of data analysing method and device based on grey wolf optimization algorithm |
| CN109737491A (en) * | 2019-01-10 | 2019-05-10 | 吉林建筑大学城建学院 | A kind of given value optimization method in campus electric heating DCS control system |
| CN109861297A (en) * | 2019-04-11 | 2019-06-07 | 上海电机学院 | A kind of power system blackstart method based on grey wolf optimization algorithm |
| CN110729939A (en) * | 2019-11-08 | 2020-01-24 | 江苏科技大学 | Parameter setting method for rotating speed ring active disturbance rejection controller of permanent magnet synchronous motor |
Non-Patent Citations (1)
| Title |
|---|
| 王正通: "改进非线性收敛因子灰狼优化算法", 《长春工业大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114992808A (en) * | 2022-06-15 | 2022-09-02 | 山东大学 | Heat pump air conditioner heat management control method and system based on combined intelligent algorithm |
| CN114992808B (en) * | 2022-06-15 | 2023-06-02 | 山东大学 | Heat pump air conditioner thermal management control method and system based on combined intelligent algorithm |
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