CN108173273B - Intelligent phase-change switch system and method for adjusting three-phase imbalance - Google Patents

Intelligent phase-change switch system and method for adjusting three-phase imbalance Download PDF

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CN108173273B
CN108173273B CN201711491987.1A CN201711491987A CN108173273B CN 108173273 B CN108173273 B CN 108173273B CN 201711491987 A CN201711491987 A CN 201711491987A CN 108173273 B CN108173273 B CN 108173273B
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current
main controller
switch
unit
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CN108173273A (en
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张磐
丁一
陈静
唐萍
温彦军
张愉
黄亮亮
凌万水
刘畅
李洁
黄潇潇
王旭东
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Shanghai Wiscom Sunest Electric Power Technology Co ltd
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Shanghai Wiscom Sunest Electric Power Technology Co ltd
State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks

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Abstract

The invention relates to an intelligent phase-change switch system for adjusting three-phase unbalance and a method thereof, which are mainly technically characterized in that: the system comprises a main controller and a phase change switch, wherein the main controller is arranged on each branch outgoing line, and the phase change switch is arranged at the front end of a user; the method comprises the steps of collecting effective values of load currents in real time and actively sending the effective values to a main controller, collecting three-phase current values on branch lines by the main controller every fixed time, calculating the unbalance degree of the three-phase currents, setting a threshold value of the unbalance degree of the three-phase currents and a threshold value of the number of times of line crossing, and starting phase change adjustment optimization strategy calculation if the calculated unbalance degree of the three-phase currents exceeds the threshold value and the number of times of line crossing exceeds the threshold value. The invention has reasonable design, achieves the steady three-phase load balance of the branch and the platform area to the maximum extent, ensures the minimum three-phase unbalance degree and the minimum switching action times, thereby improving the safety of the equipment and the reliability of power supply.

Description

Intelligent phase-change switch system and method for adjusting three-phase imbalance
Technical Field
The invention belongs to the technical field of low-voltage power distribution networks, and particularly relates to an intelligent phase-change switch system and method for adjusting three-phase unbalance.
Background
With the development of economic level, the power load is increasing day by day, and the random access of a large number of single-phase loads in a low-voltage distribution network causes a serious three-phase imbalance problem. The low-voltage distribution network three-phase imbalance can increase transformer loss and line loss, reduce the electric energy quality, cause heavy-load phase terminal low voltage, cause equipment to work normally, possibly cause single-phase overload tripping operation in serious conditions, and cause power failure accidents in transformer areas.
At present, the treatment of three-phase unbalance is mainly to adopt a reactive compensation method, equipment mainly comprises a thyristor Controlled reactor TCR (thyristor Controlled reactor) or a static Var generator SVG (static Var generator), the TCR realizes the three-phase balance of a distribution and transformation outlet through inter-phase power transfer, and the SVG realizes the three-phase balance of the distribution and transformation outlet through injecting reverse unbalance compensation current, so that the problem of actual load uniform distribution is not fundamentally solved, and the problem of three-phase unbalance needs to be solved by redistributing the load. Some improved schemes are to adopt a phase change switch to carry out three-phase unbalance compensation, and also adopt a distribution transformer terminal to control the phase change of the phase change switch, so that the three-phase balance of a distribution transformer area is realized by a method of transferring a load from a heavy-load phase to a light-load phase, but the distribution transformer area generally comprises a plurality of branch lines, so that three phases on each branch line are still unbalanced, therefore, the three-phase balance on the branch lines needs to be realized firstly, and the three-phase balance of all the branch lines also realizes the three-phase balance in the whole area. In addition, transferring the load from the heavy-duty phase to the light-duty phase only ensures immediate three-phase balance. In one day, the current value of the load is changed, and the change rules of different loads are different, so that the phase change switch can be actuated for multiple times in one day to ensure the real-time three-phase balance, the workload of the processor is increased, the universal switch is actuated for multiple times, and the loss of the switch is increased. Therefore, the way of adjusting the three-phase balance by calculating with the real-time data is not robust, and the change rule of the load needs to be summarized, so as to redistribute the three-phase load, thereby achieving the effect of three-phase balance.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an intelligent phase-change switch system and method for adjusting three-phase unbalance, which are used for reducing the action times of switching, ensuring that a load reaches a stable three-phase current balance state and improving the safety of equipment and the reliability of power supply.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
an intelligent phase change switch system for adjusting three-phase unbalance comprises a plurality of main controllers and a plurality of phase change switches, wherein the main controllers are arranged on the outgoing line of each branch line, one end of each main controller is connected into a three-phase line at the front end of the branch line, and the other end of each main controller is connected into a three-phase line at the rear end of the branch line; the phase change switch is arranged at the front end of a user, one end of the phase change switch is connected with a branch three-phase line, and the other end of the phase change switch is connected with the user; the main controllers and the phase change switches are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected below the branch line form an independent working system.
Furthermore, the main controller comprises an acquisition unit, a storage unit, a communication unit and a processor unit, wherein the processor unit is respectively connected with the acquisition unit, the storage unit and the communication unit, one end of the acquisition unit is connected to a three-phase line at the front end of the branch line, the other end of the acquisition unit is connected to the three-phase line at the rear end of the branch line through a current transformer, and the current transformer is used for acquiring three-phase current on the branch line and sending the three-phase current to the processor unit for processing; the communication unit is responsible for communication between the main controller and the commutation switch and communication between the main controller and the distribution transformer terminal, the main controller and the commutation switch are communicated in a wired or wireless mode and used for receiving load current information collected by the commutation switch and issuing a commutation adjustment command, and the main controller and the distribution transformer terminal are communicated in a wired or wireless mode and used for sending three-phase unbalanced data and a commutation processing result to the distribution transformer terminal.
Further, the commutation switch comprises an acquisition unit, a communication unit, a calculation unit, a protection unit, a control unit and a switch unit; the acquisition unit acquires load current information, voltage information and leakage current information of a user; the communication unit transmits the collected load current information data to the main controller and receives a phase change command transmitted by the main controller; the computing unit is responsible for computing whether the load current, the voltage and the leakage current reach preset protection action conditions or not, and if the load current, the voltage and the leakage current reach the preset protection action conditions, starting corresponding protection actions of the protection unit; the control unit executes a phase change command of the main controller and controls the switching unit to carry out zero-crossing phase change operation; the protection unit is provided with under-overvoltage protection, short-circuit protection, overload protection and leakage protection and is used for controlling the switch to perform protection action.
An implementation method of an intelligent phase-change switch system for adjusting three-phase unbalance comprises the following steps:
step 1, initialization: setting three-phase current unbalance degree threshold UNthAnd an overrun number threshold NthSetting the time interval t for collecting three-phase current of branch line0Setting the initial value of the number N of the unbalanced overrun of the three-phase current to be 0;
step 2, the main controller collects three-phase currents on the branch lines and calculates the unbalance degree of the three-phase currents:
UNI＝max{|IA-Iave|,|IB-Iave|,|IC-Iave|}/Iave
wherein UNIRepresenting the degree of unbalance of three-phase currents, IA、IB、ICRespectively A, B, C three-phase current values, IaveExpressing the three-phase average current, and the calculation formula is as follows;
Iave＝(IA+IB+IC)/3
unbalance degree UN of three-phase currentIWith a predetermined threshold value UN for the degree of unbalance of the three-phase currentthComparing, if the three-phase current unbalance overrun number exceeds the threshold value, adding 1 to the three-phase current unbalance overrun number N and executing the step 3, and if the three-phase current unbalance degree threshold value UN is not exceededthWait for a fixed time t0Continuing to execute the step 2;
step 3, comparing the number N of the unbalanced overrun times of the three-phase current with the threshold value N of the number of the overrun timesthIf the unbalanced number of overrun times N of the three-phase current exceeds the overrun time threshold value NthThen step 4 is executed, if not, the fixed time t is waited for0Executing the step 2;
step 4, the processing unit of the main controller reads the current data of the previous days of all the loads in the storage unit, and the arithmetic mean value of the data of the same time point of the previous days is taken as the characteristic current curve of the load in the near term;
step 5, normalizing the characteristic current curves of all loads, mapping data into a range of [0,1], classifying the loads by adopting a fuzzy C-means clustering method, classifying all the loads into different classes according to different change rules of the characteristic current curves of the loads, representing that the change rules of the current curves of the same class of loads are similar, and executing step 6 on each class of loads;
and 6, in the same type of load, performing 0-1 coding on all switch states, wherein if the switch closed phase is the A phase, the switch closed phase is [1,0 ]]TThe switch closed phase is B phase, and is [0,1,0 ]]TThe switch closed phase is C phase, and is [0,0, 1]]TAssuming that the number of switches is n, the state of the load switch can be expressed as a 3 × nThe method comprises the following steps of randomly generating an initial matrix of a switch state by using a matrix with an element value of 0 or 1, setting a target function to be the minimum branch current unbalance degree and the minimum switch action times, and searching the optimal state of a switch by adopting a differential evolution algorithm;
step 7, comparing the calculated optimal switching state with an actual switching state according to the calculated optimal switching state, determining a switch needing phase change operation, and respectively issuing a phase change instruction and a phase change phase to the phase change switch by the main controller through communication;
and 8, the phase change switch needing to perform the phase change operation receives a phase change instruction of the main controller, and the control unit controls the switch unit to execute corresponding phase switching operation.
Further, the fuzzy C-means clustering method in the step 5 comprises the following steps:
the method comprises the steps of setting a clustering number c, an iteration threshold value delta and a maximum iteration number;
step two, with a membership matrix U of random number initialization between 0 ~ 1, make the sum of all membership be 1:
Figure BDA0001535742920000041
in the formula uijThe number of the jth element in the ith group in the membership matrix U is n, and the number of the elements in each group is n;
step three, calculating a clustering center matrix C according to the U, wherein the calculating method comprises the following steps:
Figure BDA0001535742920000042
in the formula, ciIs the cluster center of the ith group in the cluster center matrix C, xjIs the jth sample value;
step four, calculating an objective function J, wherein the calculation method comprises the following steps:
Figure BDA0001535742920000043
in the formula (d)ij＝||ci-xjThe | | is the Euclidean distance from the jth data to the ith clustering center;
step fifthly, comparing the value of the target function with the target function value obtained through last iteration calculation, stopping iteration if the change amount is smaller than a set threshold value or reaches the maximum iteration number, outputting the membership matrix obtained through final calculation, turning to step six, and otherwise, turning to step six;
sixthly, calculating a new membership matrix U', wherein the calculation method comprises the following steps:
Figure BDA0001535742920000044
in formula (II) u'ijReturning to the step three for the elements of the new membership matrix U' and carrying out new iteration;
step-wise, selecting the maximum membership value as the category according to the obtained membership matrix.
Further, the differential evolution algorithm in step 6 comprises the following steps:
the method comprises the following steps: determining mutation operator F0Cross operator CR and maximum evolution algebra GmDetermining a fitness function W;
step two, randomly generating an initial population X0Evolution algebra G is 1;
step three, calculating the fitness value W of each individual in the initial population0；
Step four, judging whether the evolution algebra G reaches the maximum evolution algebra Gm(ii) a If so, terminating the evolution, and outputting the best individual at the moment as a solution; if not, continuing the step;
step fifthly, selecting three random parents to perform mutation and crossover operation, and processing boundary conditions to obtain temporary population XG1；
Step sixthly, comparing the temporary population XG1Evaluating and calculating the fitness value W in the temporary populationG1；
Step-and-shoot, on temporary and contemporary populationsSelecting to obtain new population XG；
Step base, evolution algebra G plus 1 and step four.
The invention has the advantages and positive effects that:
the invention installs the main controller on the branch line to control the commutation switch at the front end of the load to carry out commutation operation, classifies the characteristic current curve of the load by adopting a fuzzy C-means clustering method in the main controller, and searches the optimal commutation strategy of each class by adopting a differential evolution algorithm, thereby maximally achieving the steady three-phase load balance of the branch and the platform area, ensuring the minimum three-phase unbalance degree and the minimum switching action times, and further improving the safety of the equipment and the reliability of power supply.
Drawings
FIG. 1 is a schematic diagram of the connection of the intelligent commutation switch system of the invention;
FIG. 2 is a block diagram of the master control circuitry of the present invention;
FIG. 3 is a block diagram of a commutation switch circuit of the present invention;
FIG. 4 is a flow chart of the operation of the present invention;
FIG. 5 is a flow chart of the fuzzy C-means clustering method of the present invention;
FIG. 6 is a computational flow diagram of a differential evolution algorithm.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings.
An intelligent phase-change switch system for adjusting three-phase imbalance is shown in figure 1 and comprises a main controller and a phase-change switch, wherein the main controller is installed on each branch line outlet, one end of the main controller is connected into a three-phase line at the front end of each branch line, the other end of the main controller is connected into a three-phase line at the rear end of each branch line, the phase-change switch is installed at the front end of a user, one end of the phase-change switch is connected into the three-phase line of each branch line, and the other end of the phase-change switch is connected with the user. The main controllers and the phase change switches are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected below the branch line form an independent working system. The main controller classifies the characteristic current curves of the loads by adopting a fuzzy C-means clustering method, and searches an optimal commutation strategy of each class by adopting a differential evolution algorithm to realize three-phase balance on the branch line; all branch lines are in three-phase balance, and the whole distribution and transformation area is also in three-phase balance.
The main controller is used for generating a commutation strategy of a user, calculating an optimal commutation strategy by adopting a clustering algorithm and a differential evolution algorithm according to historical data, sending a commutation command to the commutation switch and controlling the commutation switch to carry out commutation action. As shown in fig. 2, the main controller mainly includes an acquisition unit, a storage unit, a communication unit, and a processor unit, wherein one end of the acquisition unit is connected to the three-phase line at the front end of the branch line, and the other end of the acquisition unit is connected to the three-phase line at the rear end of the branch line through a current transformer, and the current transformer acquires the three-phase current on the branch line and sends the three-phase current to the processor unit for processing. The communication unit is mainly responsible for communication between the main controller and the commutation switch and communication between the main controller and the distribution transformer terminal, the main controller and the commutation switch are communicated in a wired or wireless mode and used for receiving load current information collected by the commutation switch and issuing a commutation adjustment command, and the main controller and the distribution transformer terminal are communicated in a wired or wireless mode and used for sending three-phase unbalanced data and a commutation processing result to the distribution transformer terminal. The processor unit is mainly used for calculating the three-phase unbalance according to the three-phase current data and judging whether the three-phase unbalance exceeds the limit or not, if the three-phase unbalance exceeds the limit, the phase-changing strategy calculation process is started, and if the three-phase unbalance does not exceed the limit, the three-phase unbalance is continuously acquired and calculated.
As shown in fig. 3, the commutation switch mainly includes a collection unit, a communication unit, a calculation unit, a protection unit, a control unit, and a switch unit, where the collection unit mainly collects load current information, voltage information, leakage current information, and the like of a user. The communication unit mainly transmits the collected load current information data to the main controller and receives a phase change command transmitted by the main controller. The calculation unit is mainly responsible for calculating whether the load current, the voltage and the leakage current reach preset protection action conditions, and if the load current, the voltage and the leakage current reach the preset protection action conditions, the corresponding protection action of the protection unit is started. The control unit mainly executes a phase change command of the main controller and controls the switching unit to carry out zero-crossing phase change operation. The protection unit is provided with under-overvoltage protection, short-circuit protection, overload protection and leakage protection and is used for controlling the switch to perform protection action.
The working process of the intelligent phase-change switch system is as follows: the phase change switch actively transmits the effective value of the load current acquired in real time to the main controller every 1 hour, the main controller stores the current value in the storage unit, the main controller acquires the three-phase current value on the branch line every fixed time t0, calculates the unbalance degree of the three-phase current, sets a threshold UNth and an out-of-limit frequency threshold Nth of the unbalance degree of the three-phase current, and starts phase change adjustment optimization strategy calculation if the calculated unbalance degree of the three-phase current exceeds the threshold and the out-of-limit accumulated frequency also exceeds the threshold.
An intelligent commutation switch system for regulating three-phase imbalance, as shown in fig. 4, comprises the following steps:
step 1, initializing, and setting a three-phase current unbalance degree threshold UNthAnd an overrun number threshold NthSetting the time interval t for collecting three-phase current of branch line0And setting the initial value of the number N of the three-phase current unbalance overrun times to be 0.
Step 2, the main controller collects three-phase currents on the branch lines, and the unbalance degree of the three-phase currents is calculated according to the following formula:
UNI＝max{|IA-Iave|,|IB-Iave|,|IC-Iave|}/Iave
wherein UNIRepresenting the degree of unbalance of three-phase currents, IA、IB、ICRespectively A, B, C three-phase current values, IaveRepresents the three-phase average current:
Iave＝(IA+IB+IC)/3
unbalance degree UN of three-phase currentIComparing with a set three-phase current unbalance threshold, if the threshold is exceeded, adding 1 to N and executing the step 3, and if the threshold is not exceeded, waiting for a fixed time t0Step 2 is continued.
Step 3, comparing the number N of the unbalanced overrun times of the three-phase current with a threshold value NthIf N exceeds a threshold value NthThen go to step 4, if not exceedIf so, wait for a fixed time t0Step 2 is performed.
Step 4, the main controller processing unit reads the current data of 0: 00-24: 00 in 5 days before all the loads in the storage unit, Ihis＝(Id1,Id2,Id3,Id4,Id5) In which Idm＝[im1,im2,…,im24]T(m is 1,2, …,5) represents the current sampling data of 24 hours on the m-th day, and the arithmetic mean value of the data at the same time point on 5 days is taken as the characteristic current curve of the load in the near future according to the following formula:
Figure BDA0001535742920000071
and 5, normalizing the characteristic current curves of all the loads, mapping the data into a range of [0,1], classifying the loads by adopting a fuzzy C-means clustering method, classifying all the loads into different classes according to different change rules of the characteristic current curves of the loads, representing that the change rules of the current curves of the loads in the same class are similar, and executing the step 6 on each class of loads.
In this step, the characteristic current curves of all loads are normalized, and the data are mapped into the range of [0,1] as follows:
Iafter＝(Ibefore-min{[Ibefore]})/(max{[Ibefore]}-min{[Ibefore]})
in the formula Ibefore、IafterRepresent the data before and after normalization, respectively, [ I ]before]Is represented bybeforeIn the data sample set, max { [ I { [before]And min { [ I ]before]Denotes the maximum and minimum values of the data sample set, respectively.
As shown in fig. 5, the fuzzy C-means clustering method adopted in this step includes the following steps:
the method comprises the steps of setting a clustering number c, an iteration threshold value delta and a maximum iteration number.
Step two, with (0,1) between random number initialization a membership degree matrix U, make the sum of all membership degrees be 1:
Figure BDA0001535742920000081
in the formula uijIs the jth element of the ith group in the membership degree matrix U, and n is the number of the elements of each group.
Step three, calculating a clustering center matrix C according to the U, wherein the calculating method comprises the following steps:
Figure BDA0001535742920000082
in the formula, ciIs the cluster center of the ith group in the cluster center matrix C, xjIs the jth sample value.
Step four, calculating an objective function J, wherein the calculation method comprises the following steps:
Figure BDA0001535742920000083
in the formula (d)ij＝||ci-xjAnd | | is the Euclidean distance from the jth data to the ith cluster center.
Step fifthly, comparing the value of the target function with the target function value obtained through last iteration calculation, stopping iteration if the change amount is smaller than a set threshold value or reaches the maximum iteration number, outputting the membership matrix obtained through final calculation, turning to step six, and otherwise, turning to step six.
Sixthly, calculating a new membership matrix U', wherein the calculation method comprises the following steps:
Figure BDA0001535742920000091
in formula (II) u'ijIs the element of the new membership matrix U'. And returning to the step three, and performing new iteration.
Step-wise, selecting the maximum membership value as the category according to the obtained membership matrix.
And 6, in the same type of load, performing 0-1 coding on all switch states, wherein if the switch closed phase is the A phase, the switch closed phase is [1,0 ]]TThe switch closed phase is B phase, and is [0,1,0 ]]TThe switch closed phase is C phase, and is [0,0, 1]]TAssuming that the number of switches is n, the state of the load switch can be expressed as a 3 × n matrix X (X) with an element value of 0 or 1ij)(i＝1,2,3；j＝1,2,…,n；xij0 or 1), an initial matrix X of switching states is randomly generated0Setting an objective function to minimize the branch current unbalance degree and the switching action times, and searching the optimal state X of the switch by adopting the variation, crossing and selection operations of a differential evolution algorithmbest。
In this step, the limiting conditions for matrix generation are:
Figure BDA0001535742920000092
known load current data IaAnd branch actual switch state Xpre. Setting the target function as min W-min (UN) with minimum branch current unbalance and minimum switching timesI+ Σ k), where UNIFor calculating the unbalance of the three-phase current, Σ k is the number of times of actions of all switches on the branch line, and the calculation method is to change the switched state X and the actual switched state XpreThe comparison is carried out by column, and if the phase of a certain column of switches is changed, the action frequency of the switch is added with 1.
As shown in fig. 6, the specific implementation of the differential evolution algorithm includes the following steps:
the method comprises the steps of initializing and determining a mutation operator F0Cross operator CR and maximum evolution algebra GmAnd determining a fitness function W.
Step two, randomly generating an initial population X0The evolution algebra G is 1.
Step three, calculating the fitness value W of each individual in the initial population0。
Step four, judging an evolution algebra GWhether maximum evolution algebra G is reachedm. If so, terminating the evolution, and outputting the best individual at the moment as a solution; if not, continuing the step fifthly.
Step fifthly, selecting three random parents to perform mutation and crossover operation, and processing boundary conditions to obtain temporary population XG1。
Step sixthly, comparing the temporary population XG1Evaluating and calculating the fitness value W in the temporary populationG1。
Step-and-night, and selecting temporary population and contemporary population to obtain new population XG。
Step (i), evolution algebra G plus 1, and fourth step.
And 7, comparing the calculated optimal switching state with the actual switching state according to the calculated optimal switching state, determining a switch needing phase change operation, and respectively issuing a phase change instruction and a phase change phase to the phase change switch by the main controller through communication.
And 8, the phase change switch needing to perform the phase change operation receives a phase change instruction of the main controller, and the control unit controls the switch unit to execute corresponding phase switching operation.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.

Claims (2)

1. The utility model provides an adjust unbalanced intelligent commutation switching system of three-phase which characterized in that: the phase-change control circuit comprises a plurality of main controllers and a plurality of phase-change switches, wherein the main controllers are arranged on the outgoing lines of each branch line, one end of each main controller is connected into a three-phase line at the front end of the branch line, and the other end of each main controller is connected into a three-phase line at the rear end of the branch line; the phase change switch is arranged at the front end of a user, one end of the phase change switch is connected with a branch three-phase line, and the other end of the phase change switch is connected with the user; the main controllers and the phase change switches are in communication connection in a wired or wireless mode, and the main controller on each branch line and all the phase change switches connected below the branch line form an independent working system;
the main controller comprises an acquisition unit, a storage unit, a communication unit and a processor unit, wherein the processor unit is respectively connected with the acquisition unit, the storage unit and the communication unit, one end of the acquisition unit is connected to a three-phase line at the front end of a branch line, the other end of the acquisition unit is connected to a three-phase line at the rear end of the branch line through a current transformer, and three-phase current on the branch line is acquired through the current transformer and is sent to the processor unit for processing; the communication unit is responsible for communication between the main controller and the commutation switch and communication between the main controller and the distribution transformer terminal, the main controller and the commutation switch are communicated in a wired or wireless mode and used for receiving load current information acquired by the commutation switch and issuing a commutation adjustment command, and the main controller and the distribution transformer terminal are communicated in a wired or wireless mode and used for sending three-phase unbalanced data and a commutation processing result to the distribution transformer terminal;
the phase change switch comprises an acquisition unit, a communication unit, a calculation unit, a protection unit, a control unit and a switch unit; the acquisition unit acquires load current information, voltage information and leakage current information of a user; the communication unit transmits the collected load current information data to the main controller and receives a phase change command transmitted by the main controller; the computing unit is responsible for computing whether the load current, the voltage and the leakage current reach preset protection action conditions or not, and if the load current, the voltage and the leakage current reach the preset protection action conditions, starting corresponding protection actions of the protection unit; the control unit executes a phase change command of the main controller and controls the switching unit to carry out zero-crossing phase change operation; the protection unit is provided with under-overvoltage protection, short-circuit protection, overload protection and leakage protection and is used for controlling the switch to perform protection action.
2. A method for implementing the intelligent commutation switch system for regulating three-phase imbalance according to claim 1, wherein: the method comprises the following steps:
step 1, initialization: setting three-phase current unbalance degree threshold UNthAnd an overrun number threshold NthSetting the time interval t for collecting three-phase current of branch line0Setting the initial value of the number N of the unbalanced overrun of the three-phase current to be 0;
step 2, the main controller collects three-phase currents on the branch lines and calculates the unbalance degree of the three-phase currents:
UNI＝max{|IA-Iave|,|IB-Iave|,|IC-Iave|}/Iave
wherein UNIRepresenting the degree of unbalance of three-phase currents, IA、IB、ICRespectively A, B, C three-phase current values, IaveExpressing the three-phase average current, and the calculation formula is as follows;
Iave＝(IA+IB+IC)/3
unbalance degree UN of three-phase currentIWith a predetermined threshold value UN for the degree of unbalance of the three-phase currentthComparing, if the three-phase current unbalance overrun number exceeds the threshold value, adding 1 to the three-phase current unbalance overrun number N and executing the step 3, and if the three-phase current unbalance degree threshold value UN is not exceededthWait for a fixed time t0Continuing to execute the step 2;
step 3, comparing the number N of the unbalanced overrun times of the three-phase current with the threshold value N of the number of the overrun timesthIf the unbalanced number of overrun times N of the three-phase current exceeds the overrun time threshold value NthThen step 4 is executed, if not, the fixed time t is waited for0Executing the step 2;
step 4, the processing unit of the main controller reads the current data of the previous days of all the loads in the storage unit, and the arithmetic mean value of the data of the same time point of the previous days is taken as the characteristic current curve of the load in the near term;
step 5, normalizing the characteristic current curves of all loads, mapping data into a range of [0,1], classifying the loads by adopting a fuzzy C-means clustering method, classifying all the loads into different classes according to different change rules of the characteristic current curves of the loads, representing that the change rules of the current curves of the same class of loads are similar, and executing step 6 on each class of loads;
step 6, in the same type of load, all the switches are in the shape of a switchThe state is coded by 0-1, and the switch closed phase is A phase, then [1,0 ]]TThe switch closed phase is B phase, and is [0,1,0 ]]TThe switch closed phase is C phase, and is [0,0, 1]]TIf the number of the switches is n, the state of the load switch can be represented as a matrix with the element value of 0 or 1 of 3 multiplied by n, an initial matrix of the switch state is randomly generated, an objective function is set to be that the branch current unbalance degree is minimum and the switching action times are minimum, and the optimal state of the switch is found by adopting a differential evolution algorithm;
step 7, comparing the calculated optimal switching state with an actual switching state according to the calculated optimal switching state, determining a switch needing phase change operation, and respectively issuing a phase change instruction and a phase change phase to the phase change switch by the main controller through communication;
step 8, the phase change switch needing to perform the phase change operation receives a phase change instruction of the main controller, and the control unit controls the switch unit to execute corresponding phase change operation;
the fuzzy C-means clustering method in the step 5 comprises the following steps:
the method comprises the steps of setting a clustering number c, an iteration threshold value delta and a maximum iteration number;
step two, with a membership matrix U of random number initialization between 0 ~ 1, make the sum of all membership be 1:
Figure FDA0003292690470000031
in the formula uijThe number of the jth element in the ith group in the membership matrix U is n, and the number of the elements in each group is n;
step three, calculating a clustering center matrix C according to the U, wherein the calculating method comprises the following steps:
Figure FDA0003292690470000032
in the formula, ciIs the cluster center of the ith group in the cluster center matrix C, xjIs the jth sample value;
step four, calculating an objective function J, wherein the calculation method comprises the following steps:
Figure FDA0003292690470000033
in the formula (d)ij＝||ci-xjThe | | is the Euclidean distance from the jth data to the ith clustering center;
step fifthly, comparing the value of the target function with the target function value obtained through last iteration calculation, stopping iteration if the change amount is smaller than a set threshold value or reaches the maximum iteration number, outputting the membership matrix obtained through final calculation, turning to step six, and otherwise, turning to step six;
sixthly, calculating a new membership matrix U', wherein the calculation method comprises the following steps:
Figure FDA0003292690470000034
in formula (II) u'ijReturning to the step three for the elements of the new membership matrix U' and carrying out new iteration;
step-wise, selecting the maximum membership value as the category according to the obtained membership matrix;
the differential evolution algorithm in the step 6 comprises the following steps:
the method comprises the following steps: determining mutation operator F0Cross operator CR and maximum evolution algebra GmDetermining a fitness function W;
step two, randomly generating an initial population X0Evolution algebra G is 1;
step three, calculating the fitness value W of each individual in the initial population0；
Step four, judging whether the evolution algebra G reaches the maximum evolution algebra Gm(ii) a If so, terminating the evolution, and outputting the best individual at the moment as a solution; if not, continuing the step;
step fifthly, selecting three random parents to perform mutation and crossover operation, and carrying out opposite side operationProcessing boundary conditions to obtain a temporary population XG1；
Step sixthly, comparing the temporary population XG1Evaluating and calculating the fitness value W in the temporary populationG1；
Step-and-night, and selecting temporary population and contemporary population to obtain new population XG；
Step base, evolution algebra G plus 1 and step four.
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