Mirish,

I couldn't help notice that you're building this model from scratch (well,
from a database) and you mentioned *"**To make the problem simple I used
all buses as PQ buses except one slack bus"*.   This actually makes it
harder to converge, unless you have *very* accurate data on what the
reactive injections Q (on generator buses) should be.

May I suggest a different, incremental approach:

   1. Start by keeping all generator buses you can as PV, instead of PQ.
   They will help holding up the voltage profile.  After all, a PV node is a
   slack bus in what regards the reactive power injection.
   2. For the loads, start by zeroing out PD (real power demand), but
   keeping QD (reactive demand)
   3. For generators, set the scheduled PG to zero
   4. For lines & transformers, zero out the resistance R
   5. The resulting network will be a "purely reactive power" model. Now
   run a powerflow.  If this doesn't have a feasible powerflow solution, it is
   because some branches have an X parameter that is too large (or
   equivalently, some load QD is too large).  Ramp down the profile of QD
   until you see convergence.
   6. Look at the resulting Q flows across branches, and try to detect
   anomalously large values (i.e. clear outliers). They will help you uncover
   values of X that may be wrong (too large).  Also, keep an eye on negative X
   coming from equivalents such as 3-winding transformers; they may also be
   wrong.
   7. Once you get that working, ramp up the values of PD on loads and PG
   on generators (keeping an eye on the swing's resulting PG, in order to
   redistribute big excesses).
   8. Finally ramp up the resistance on lines.

The whole idea is based on the fact that, for transmission networks (lines
with R<<X), the reactive flows are like the "backbone" on which real power
flows can sort of "ride on".  Get a healthy backbone first, and then you
can start transporting real power.

Hope it helps,

-- 
Jose L. Marin
Gridquant España SL
Grupo AIA


On Wed, Aug 12, 2015 at 2:36 AM, Mirish Thakur <mirishtha...@gmail.com>
wrote:

> 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
> <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)
>  it    objective   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     0.727712    0.0545952      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. <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 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 <mirishtha...@gmail.com>
>> Reply-To: MATPOWER discussion forum <matpowe...@list.cornell.edu>
>> Date: Monday, August 10, 2015 at 10:44 AM
>> To: MATPOWER discussion forum <matpowe...@list.cornell.edu>
>> 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 satisfies active and
>> reactive  power demand. For renewable generation, I specified as negative
>> demand on respective buses. I checked all possibilities mentioned in  FAQ (
>> http://www.pserc.cornell.edu/matpower/#pfconvergence ) but couldn’t
>> figure out problem. Also I checked (case_info) to see any island but got
>> full system without island. To make the problem simple I used all buses as
>> PQ buses except one slack bus. Also my casefile converges for rundcpf but
>> fails to runpf and gives error like ‘Newton's method power flow did not
>> converge in 10 iterations.’ Also I found that when I use following code-
>>
>>
>>      opt  = mpoption('OUT_BUS', 0, 'OUT_BRANCH', 0, 'VERBOSE', 2);
>>
>>    mpc  = loadcase('casefile');
>>
>>  results =runpf(mpc,opt);
>>
>>
>> may be it gives me divergence of PQ mismatch instead of convergence.
>>
>>
>> MATPOWER Version 5.1, 20-Mar-2015 -- AC Power Flow (Newton)
>>
>>
>>
>>  it    max P & Q mismatch (p.u.)
>>
>> ----  ---------------------------
>>
>>   0         2.296e+01
>>
>>   1         1.729e+01
>>
>>   2         2.450e+03
>>
>>   3         2.352e+03
>>
>>   4         6.962e+06
>>
>>   5         1.740e+06
>>
>>   6         4.352e+05
>>
>>   7         1.753e+07
>>
>>   8         4.382e+06
>>
>>   9         3.322e+06
>>
>>  10         8.303e+05
>>
>> Newton's method power flow did not converge in 10 iterations.
>>
>>
>>
>> >>>>>  Did NOT converge (0.23 seconds)  <<<<<
>>
>>
>>
>>
>>
>> results =
>>
>>         version: '2'
>>
>>     baseMVA: 100
>>
>>              bus: [1086x13 double]
>>
>>              gen: [467x21 double]
>>
>>          branch: [2145x17 double]
>>
>>             order: [1x1 struct]
>>
>>                 et: 0.2320
>>
>>        success: 0
>>
>> I will be very thankful for your help.
>>
>>
>> Regards
>>
>> Mirish Thakur.
>>
>> KIT, University.
>>
>>
>

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