Re: convergence problem in runpf.

2015-08-11 Thread Abhyankar, Shrirang G. 
Unfortunately, I can’t say anything about the divergence of the power flow by 
looking at the Jacobian spy plot. The Jacobian spy plot looks fine to me based 
on how the equations and variables are ordered for MATPOWER’s power flow. 
Please send me the test case (offline) and I’ll try to debug further.

Shri

You could also try to check whether your case is insolvable 
http://www.pserc.cornell.edu/Matpower/docs/ref/matpower5.1/extras/sdp_pf/insolvablepfsos.html



From: Mirish Thakur mailto:mirishtha...@gmail.com>>
Reply-To: MATPOWER discussion forum 
mailto:matpowe...@list.cornell.edu>>
Date: Tuesday, August 11, 2015 at 7:36 PM
To: MATPOWER discussion forum 
mailto:matpowe...@list.cornell.edu>>
Subject: Re: convergence problem in runpf.

Dear Mr.Shree,

Thank you very much for your help. As per your suggestion and FAQ I tried to 
find out the problems.
The results I got-
1) Fast-decoupled power flow did not converge in 30 iterations.
2) By following   http://www.pserc.cornell.edu/matpower/#pfconvergence  I tried 
to runcpf to get good  initial guess and i got results like
step   1 : lambda =  0.084, corrector did not converge in 10 iterations. Where 
lambda is < 1 and for reducing steady state loading limitation I reduced demand 
less than 60 % which also failed to converge the power flow.
3) Also I tried to run an optimal power flow according to Dr. Ray's explanation 
 given in following link-

https://www.mail-archive.com/search?l=matpower-l@cornell.edu&q=subject:%22Re%5C%3A+%5C%5BMatpower%5C%5D+3500+bus+simulation%22&o=newest

but got the results like-

MATPOWER Version 5.1, 20-Mar-2015 -- AC Optimal Power Flow
MATLAB Interior Point Solver -- MIPS, Version 1.2, 20-Mar-2015
 (using built-in linear solver)
 itobjective   step size   feascond gradcond compcond costcond
   -    
  0 1200199.7 2.41677 0.71  536.7620
  1 946197.39 15.531   1.3682  1.75871  525.914 0.209885
  2 954529.91 15.405 0.766107 0.203773  297.341   0.00871422
  3  954849.8 12.849 0.7277120.0545952  258.471   0.00033166
  4 954629.03  13035  0.69114 0.107402  258.048  0.000228815
  5 954614.88  33406 0.692682 0.255673  257.828  1.46744e-05
  6 954525.69  14111 0.579613 0.143897  256.765  9.24569e-05
  7 954539.42  61648 0.581139 0.501345  255.994  1.42362e-05
  8 954518.93  22452 0.573652 0.478609  255.465  2.12443e-05
  9 954494.92 8540.4 0.556318 0.403754  254.653  2.48944e-05
 10 954523.58  20366 0.556265 0.570707  254.104  2.97206e-05
 11 954522.07 6142.4 0.554989 0.647881  256.561  1.57288e-06
 12 954573.42 6192.9 0.513972 0.716706  253.604  5.32434e-05
 13 954575.97 5912.1 0.509457 0.699751  252.612  2.64406e-06
 14 954576.23  16534 0.509454 0.674865  253.278  2.64555e-07
 15 954579.65  12324 0.509394 0.812237  252.966  3.54362e-06
 16 954579.86 7650.3 0.509391  0.80973  252.948  2.18359e-07
 17 954579.87 8185.1 0.509391 0.809591  252.947  1.48635e-08
 18 954579.88 8696.2 0.509391 0.809411  252.945  1.31087e-08
 19  954579.9 9392.5  0.50939  0.80927  252.943   1.3818e-08
Numerically Failed

Did not converge in 19 iterations.

>  Did NOT converge (3.71 seconds)  <

4) But when I used spy(J) , to look jacobian matrix it gives me some strange 
distribution. Herewith I attached image of jacobian matrix. ( I have modeled 
transmission lines and transformers to get one single branch matrix e.g. 
branch_matrix=vertcat(transmission_lines,grid_transformer) which is similar to 
matpower test cases.). So could you please suggest me what necessary steps I 
should follow?
Thank you for your time.

Regards
Mirish Thakur
KIT, University.

On Mon, Aug 10, 2015 at 7:14 PM, Abhyankar, Shrirang G. 
mailto:abhy...@anl.gov>> wrote:
I would suggest trying the following:


  1.  Use the solution of a fast decoupled power flow or an optimal power flow 
(with line limits and voltage limits relaxed) as the initial guess for the 
power flow.
  2.  Follow step 5 in http://www.pserc.cornell.edu/matpower/#pfconvergence 
making CPF to stop when the nose-point is reached. This can be done via results 
= runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,’NOSE’)). If 
results.cpf.max_lam is >= 1, then it shows that the initial guess for the power 
flow is the problem for its divergence. To obtain a ‘good’ initial guess, run 
the continuation power flow again making it to stop exactly at lam = 1 (the 
target case loading and generation) via results = 
runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,1.0)). You can then save the 
results struct as a matp

Re: convergence problem in runpf.

2015-08-11 Thread Mirish Thakur 
Dear Mr.Shree,

Thank you very much for your help. As per your suggestion and FAQ I tried
to find out the problems.
The results I got-
1) Fast-decoupled power flow did not converge in 30 iterations.
2) By following   http://www.pserc.cornell.edu/matpower/#pfconvergence  I
tried to runcpf to get good  initial guess and i got results like
step   1 : lambda =  0.084, corrector did not converge in 10 iterations.
Where lambda is < 1 and for reducing steady state loading limitation I
reduced demand less than 60 % which also failed to converge the power flow.
3) Also I tried to run an optimal power flow according to Dr. Ray's
explanation  given in following link-


*https://www.mail-archive.com/search?l=matpower-l@cornell.edu&q=subject:%22Re%5C%3A+%5C%5BMatpower%5C%5D+3500+bus+simulation%22&o=newest
*

but got the results like-

MATPOWER Version 5.1, 20-Mar-2015 -- AC Optimal Power Flow
MATLAB Interior Point Solver -- MIPS, Version 1.2, 20-Mar-2015
 (using built-in linear solver)
 itobjective   step size   feascond gradcond compcond
costcond
   -   

  0 1200199.7 2.41677 0.71  536.762
   0
  1 946197.39 15.531   1.3682  1.75871  525.914
0.209885
  2 954529.91 15.405 0.766107 0.203773  297.341
0.00871422
  3  954849.8 12.849 0.7277120.0545952  258.471
0.00033166
  4 954629.03  13035  0.69114 0.107402  258.048
 0.000228815
  5 954614.88  33406 0.692682 0.255673  257.828
 1.46744e-05
  6 954525.69  14111 0.579613 0.143897  256.765
 9.24569e-05
  7 954539.42  61648 0.581139 0.501345  255.994
 1.42362e-05
  8 954518.93  22452 0.573652 0.478609  255.465
 2.12443e-05
  9 954494.92 8540.4 0.556318 0.403754  254.653
 2.48944e-05
 10 954523.58  20366 0.556265 0.570707  254.104
 2.97206e-05
 11 954522.07 6142.4 0.554989 0.647881  256.561
 1.57288e-06
 12 954573.42 6192.9 0.513972 0.716706  253.604
 5.32434e-05
 13 954575.97 5912.1 0.509457 0.699751  252.612
 2.64406e-06
 14 954576.23  16534 0.509454 0.674865  253.278
 2.64555e-07
 15 954579.65  12324 0.509394 0.812237  252.966
 3.54362e-06
 16 954579.86 7650.3 0.509391  0.80973  252.948
 2.18359e-07
 17 954579.87 8185.1 0.509391 0.809591  252.947
 1.48635e-08
 18 954579.88 8696.2 0.509391 0.809411  252.945
 1.31087e-08
 19  954579.9 9392.5  0.50939  0.80927  252.943
1.3818e-08
Numerically Failed

Did not converge in 19 iterations.

>  Did NOT converge (3.71 seconds)  <

4) But when I used spy(J) , to look jacobian matrix it gives me some
strange distribution. Herewith I attached image of jacobian matrix. ( I
have modeled transmission lines and transformers to get one single branch
matrix e.g. branch_matrix=vertcat(transmission_lines,grid_transformer)
which is similar to matpower test cases.). So could you please suggest me
what necessary steps I should follow?
Thank you for your time.

Regards
Mirish Thakur
KIT, University.

On Mon, Aug 10, 2015 at 7:14 PM, Abhyankar, Shrirang G. 
wrote:

> I would suggest trying the following:
>
>
>1. Use the solution of a fast decoupled power flow or an optimal power
>flow (with line limits and voltage limits relaxed) as the initial guess for
>the power flow.
>2. Follow step 5 in
>http://www.pserc.cornell.edu/matpower/#pfconvergence making CPF to
>stop when the nose-point is reached. This can be done via results =
>runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,’NOSE’)). If
>results.cpf.max_lam is >= 1, then it shows that the initial guess for the
>power flow is the problem for its divergence. To obtain a ‘good’ initial
>guess, run the continuation power flow again making it to stop exactly at
>lam = 1 (the target case loading and generation) via results =
>runcpf(mpcbase,mpctarget,mpoption(‘cpf.stop_at’,1.0)). You can then save
>the results struct as a matpower case file (via savecase()). On the other
>hand, if results.cpf.max_lam < 1, then the loading/generation in your
>original case is beyond the system steady-state loading limit.
>
> Shri
> From: Mirish Thakur 
> Reply-To: MATPOWER discussion forum 
> Date: Monday, August 10, 2015 at 10:44 AM
> To: MATPOWER discussion forum 
> Subject: convergence problem in runpf.
>
> Dear Matpower Community,
>
>
> I’m working on power flow project and have used grid data from database. I
> have modelled all line parameters (R X B) in p.u. system, also same for
> transformers and kept generator output until it