Artificial Neural Network Approach for Solving Power Flow Problem: A Case Study of Ayede 132 KV Power System, Nigeria

2011 ◽  
Vol 367 ◽  
pp. 133-141
Author(s):  
P.B. Osofisan ◽  
J.O. Ilevbare
Keyword(s):  
Neural Network ◽  
Power System ◽  
Test Data ◽  
Power Flow ◽  
Flow Problem ◽  
High Accuracy ◽  
Voltage Phase ◽  
Phase Angles ◽  
Very High ◽  
Power Flow Problem

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.

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

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil S. Damodhar
Keyword(s):  
Steady State ◽  
Power System ◽  
Conventional Method ◽  
Power Flow ◽  
Flow Problem ◽  
Phase Shifting ◽  
Outer Loop ◽  
Facts Devices ◽  
System Components ◽  
Power Flow Problem

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.

Applications of Homotopy Algorithm for Solving Optimal Power Flow of Power System

2014 ◽  
Vol 494-495 ◽  
pp. 1627-1630
Author(s):  
Xiao Ying Zhang ◽  
Ning Ding ◽  
Chen Li
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Alternating Current ◽  
Homotopy Method ◽  
Homotopy Algorithm ◽  
Optimal Power ◽  
Novel Algorithm ◽  
Power Flow Problem

This paper introduces an homotopy algorithm which has convergence stability to solve the alternating current optimal power flow problem. The complicated Alternating Current Power Flow (ACPF) can simplify as simple Direct Current Power Flow (DCPF). The homotopy participation factor is introduced into the linear DCPF to make DCPF convert back into ACPF gradually to realize Alternating Current Power Flow Homotopy method (ACPFH). The homotopy curves are generated to solve a series of nonlinear problems.The traditional method can not solve the unstable points,because the calculate process always turn up Jacobian matrix.But the Homotopy method can calculate all results. It is a superiority for Homotopy,and then can explore power system problem more entirety.This novel algorithm is different from Newton - Raphson method, because it isnt sensitive to the initial point selection and has the global convergence.The homotopy algorithm is applied to IEEE - 3, 9, 14, 30, 36, 57, 118 node testing systems for power flow optional calculation, the simulation results show that the novel algorithm can solve power flow problem better and its calculating speed is much faster than the traditional algorithm, it can calculate the optimal value more direct.

scholarly journals A Bio-inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Manzoor Ahmad ◽  
Nadeem Javaid ◽  
Iftikhar Azim Niaz ◽  
Ahmad Almogren ◽  
Ayman Radwan
Keyword(s):  
Power System ◽  
Heuristic Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Optimal Power ◽  
Power Flow Problem

scholarly journals An Efficient Hybrid Method for Solving Security-Constrained Optimal Power Flow Problem

2020 ◽  
Vol 12 (4) ◽  
pp. 933-955
Author(s):  
Phuong Minh Le ◽  
◽  
Thanh Long Duong ◽  
Dieu Ngoc Vo ◽  
Tung Thanh Le ◽  
...  
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Flow Problem ◽  
Optimal Operation ◽  
Optimal Power ◽  
Gradient Based ◽  
Constriction Factor ◽  
Power Flow Problem

The optimal operation for different states such as normal and contingency cases of a power system has a very important role in the operation. Therefore, it is necessary to analyze contingencies in the system so as the most severe cases should be considered for integrating into the optimal power flow (OPF) problem and the security-constrained optimal power flow (SCOPF) becomes an important problem for considering in the power system operation. This paper proposes a combined pseudo-gradient based particle swarm optimization with constriction factor (PGPSO) and the differential evolution (DE) method for solving the SCOPF problem. The PGPSO-DE method is a newly developed method for utilizing the advantages of the pseudogradient guided PSO method with a constriction factor and the DE method. The proposed PGPSO-DE has been tested on the IEEE 30 bus system for the normal case and the contingency case with two types of the objective function. The results yielded from the proposed method have been validated via comparing to those from the conventional PSO, DE, and other methods reported in the literature. The comparisons for the results obtained from the proposed PGPSODE method have shown that it is very effective to solve the large-scale and complex SCOPF problem.

scholarly journals Particle Swarm Optimization- Artificial Neural Network For Power System Load Flow

2012 ◽  
pp. 180-189
Author(s):  
Avnish Singh ◽  
Shishir Dixit ◽  
L. Srivastava
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Flow ◽  
Flow Problem ◽  
Particle Swarm ◽  
Load Flow ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Bus Voltage

Load flow study is done to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state working condition of a power system. It is important and most frequently carried out study performed by power utilities for power system planning, optimization, operation and control. In this paper a Particle Swarm Optimization Neural Network (PSO-ANN) is proposed to solve load flow problem under different loading/ contingency conditions for computing bus voltage magnitudes and angles of the power system. A multilayered feed-forward neural network is trained by using PSO technique. The results show the effectiveness of the proposed PSO-ANN based approach for solving power flow problem having different loading conditions and single-line outage contingencies in IEEE 14 bus system

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