Patent ID: 11942784
Assignee: SICHUAN UNIVERSITY
Field: Electrical machinery, apparatus, energy (Electrical engineering)
Classification: CPC G  H  Y | IPC H

Claim 2:
3. The method for formulating the coordinated action strategy of SSTS and DVR for voltage sag mitigation according to claim 1, wherein a grouping mitigation optimization of the sensitive loads in step 1.3 specifically comprises:
1) setting an objective function:
setting the grouping matrix [α0, α1, . . . ,αn]i=[αj]i, wherein i represents an i-th group, αj=0 or 1, αj=0 indicates that the sensitive load is not in the i-th group, and αj=1 indicates that the sensitive load is in the i-th group;
a first optimization objective is to minimize the sum of the capacities of installed DVRs:, min
      ⁢
        
      
       S
       DVR
      
     
     =
     
      
       ∑
       
        i
        =
        1
       
       N
      
      
       (
       
        
         
          U
          i
         
         
          U
          n
         
        
        ·
        
         S
         
          load
          -
          i
         
        
       
       )
      
     
    
   
   
    
     (
     5
     )
    
   
  
 

wherein SDVR is the sum of the capacities of i DVRs, N is the number of groups of DVRs, Uiis the compensation voltage of the i-th group, Un is the rated voltage of the sensitive user, and Sload-i, is the sum of the capacities of the i-th group of sensitive loads to be governed;
a second optimization objective is to minimize the interruption probability of a sensitive industrial process:, min
      ⁢
        
      
       P
       process
      
     
     =
     
      
       ∑
       
        m
        =
        1
       
       M
      
      
       P
       
        process
        -
        m
       
      
     
    
   
   
    
     (
     6
     )
    
   
  
 

wherein Pprocess-m is the interruption probability of an m-th sensitive industrial process;
2) Setting constraints
{circle around (1)} capacity constraints of the sensitive loads, S
      
       load
       -
       i
      
     
     =
     
      
       ∑
       
        j
        =
        1
       
       n
      
      
       (
       
        
         α
         j
        
        ⁢
        
         S
         j
        
       
       )
      
     
    
   
   
    
     (
     7
     )
    
   
  
 

wherein Sj is the rated capacity of a jth sensitive load;
{circle around (2)} tripping probability constraints of the sensitive loads
the tripping probability Pj of a single sensitive load is:

Pj=∫∫Ωp(Tsag)p(Usag)dUsagdTsag  (8)

wherein Usag and Tsag are amplitude and duration time of voltage sag respectively; p(Usag) and p(Tsag) are probability density functions of the amplitude and the duration time of voltage sag respectively; Ω is a fault region determined by a load VTC; the specific data of each sensitive load is substituted into the above formula to obtain PS1-j, PS2-j, and PS3-j;
{circle around (3)} DVR compensation voltage constraints
Ui is a compensation voltage amplitude that the DVR installed in the i-th group should output, i.e., a maximum value of compensation voltage required by the sensitive load with αj=1 in the grouping matrix of the i-th group, and an expression is:

Ui=max {Uα0, Uα1, . . . , Uαn|αj=1}  (9)

Uαj≤Udemand-αj  (10)

wherein Uαj is the compensation voltage of the jth sensitive load, and Udemand-αi is the highest compensation voltage of the jth sensitive load to satisfy the requirement for voltage sag mitigation;
{circle around (4)} grouping constraints of the sensitive loads
there are only two cases for the grouping of any sensitive load:
a. the sensitive load does not belong to any group, i.e.: αj=0∈[α0,α1, . . . ,αn]i,and αj=0∈[α0,α1, . . . ,αn]else-i;
b. if the sensitive load is divided into a certain group, the sensitive load is and can only be in the group; i.e.: when αj=1∈[α,α1, . . . ,αn]i, αj=0∈[α0,α1, . . . ,αn]else-i;
wherein [α0,α1, . . . ,αn]else-i is a grouping matrix of other groups except the i-th group;
3) model solving
solving a Pareto optimal solution set of the model by an NSGA-II algorithm, and giving satisfaction to each objective function corresponding to each group of solutions in the Pareto optimal solution set by a slightly small fuzzy satisfaction function, as shown in formula (11):, μ
      vo
     
     =
     
      {
      
       
        
         
          1
          ,
         
        
        
         
          
           f
           vo
          
          ≤
          
           f
           
            o
            ⁢
               
            min
           
          
         
        
       
       
        
         
          
           
            
             f
             
              o
              ⁢
                
              max
             
            
            -
            
             f
             vo
            
           
           
            
             f
             
              o
              ⁢
                
              max
             
            
            -
            
             f
             
              o
              ⁢
                
              min
             
            
           
          
          ,
         
        
        
         
          
           f
           
            o
            ⁢
               
            min
           
          
          ≤
          
           f
           vo
          
          ≤
          
           f
           
            o
            ⁢
              
            max
           
          
         
        
       
       
        
         
          0
          ,
         
        
        
         
          
           f
           vo
          
          ≥
          
           f
           
            o
            ⁢
               
            min
           
          
         
        
       
      
     
    
   
   
    
     (
     11
     )
    
   
  
 

in the formula, o∈{1,2, . . . ,O}; O is the number of objective functions; μvo is the satisfaction of an oth objective function corresponding to a vth group of Pareto solutions; fvo is a function value of the oth objective function corresponding to the vth group of solutions in the Pareto solution set; f0min is a minimum value of the function values of the oth objective function corresponding to all the solutions in the Pareto solution set; and f0max is a maximum value of the function values of the oth objective function corresponding to all the solutions in the Pareto solution set;
solving the satisfaction μv, of each Pareto solution based on the satisfaction of each objective function corresponding to each Pareto solution;, μ
      v
     
     =
     
      
       1
       O
      
      ⁢
      
       
        ∑
        
         v
         =
         1
        
        O
       
       
        μ
        vo
       
      
     
    
   
   
    
     (
     12
     )
    
   
  
 

using a Pareto solution with largest satisfaction μv as a final solution of a decision variable.