Novel models of power system components for implicit solution of the adjusted power flow problem

Author(s):  
Sunil S. Damodhar

Abstract The solution of the adjusted power flow problem involves handling power system components whose control characteristics possess operational limits. Examples include generator reactive power limits, tap-changing and phase-shifting transformers, and FACTS devices. While the conventional method involves checking for limit violations in an outer loop drawn around the unadjusted power flow problem being solved by the Newton-Raphson (NR) method, for iterative processes, it is desirable to have smooth, continuously differentiable models implicitly handled within a single loop. A novel formulation for a subset of devices is presented for implicit handling within power flow. The steady state characteristics of tap-changing and phase-shifting transformers, and FACTS devices SVC and STATCOM, can be described using the “cut function”, a piecewise linear function traditionally employed in neural networks. A new approximation of the cut function is used for formulating novel equations describing the steady state characteristics. An augmented set of equations is formed and solved by the NR method, eliminating the need of an outer loop. The efficacy of the proposed method is demonstrated by employing it for plotting bus voltage profiles and determining maximum loadability of test systems. Comparisons with the conventional method show that significant savings in computation can be achieved.

2011 ◽  
Vol 367 ◽  
pp. 133-141
Author(s):  
P.B. Osofisan ◽  
J.O. Ilevbare

The main objective of this research work was to use Artificial Neural Network (ANN) based method for solving Power Flow Problem for a power system in Nigeria. This was achieved using the Backpropagation (multilayered feed-forward) Neural Network model. Two Backpropagation neural networks were designed and trained; one for computing voltage magnitudes on all buses and the other for computing voltage phase angles on all PV and PQ buses for different load and generation conditions for a 7-bus 132 kV power system in South-West Nigeria (Ayede). Due to unavailability of historical field records, data representing different scenarios of loading and/or generation conditions had to be generated using Newton-Raphson non-linear iterative method. A total of 250 scenarios were generated out of which 50% were used to train the ANNs, 25% were used for validation and the remaining 25% were used as test data for the ANNs. The test data results showed very high accuracy for the ANN used for computing voltage magnitudes for all test data with a Mean Square Error (MSE) of less than 10-6. Also, the ANN used for computing voltage phase angles showed very high accuracy in about 80% of the test data and acceptable results in about 97% of the test data. The MSE for all the test data results for the ANN computing voltage phase angles was less than 10-2.


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