Abstract
Electric power system is a highly complex and non linear system. Its analysis and control in real time environment requires highly sophisticated computational skills. Computations are reaching a limit as far as conventional computer based algorithms are concerned. It is therefore required to find out newer methods which can be easily implemented on dedicated hardware. It is a very difficult task due to complexity of the power system with all its interdependent variables, thus making the neural networks one of the better options for the solution of different issues in operation and control. In this project an attempt has been made to implement ANN’s for observability determination, State Estimation, Economic Load Dispatch and for Reactive Power Optimization. A Hopfield neural network model has been developed to test Topological Observability of Power System and it is tested on two different test systems. The results so obtained, are comparable with those results of conventional root based observability determination technique. Further a Hopfield model has been developed to determine State Estimation of power system. State Estimation of 6 bus system and IEEE 14 bus system is attempted using this Hopfield neural network. Results obtained by developed model are compared with those of conventional Non Linear WLS State Estimation. Next use of ANN for Economic Load Dispatch problem has been developed. Economic Load dispatch has been studied using various test system data (like 3, 6, 20 & 30 units) and the results are compared with conventional Lambda iterative technique and Particle Swarm Optimization techniques. Next Reactive Power Optimization problem has been attempted using ANN. The performance of so developed ANN is tested on Ward Hale 6 bus system and IEEE 30 bus system data and the results obtained are compared with those of the results obtained by GA and Particle Swarm Optimization technique. Keywords : ANN, Load dispatch, optimization DOI : 10.7176/ISDE/10-6-01 Publication date :July 31st 2019
Highlights
Modern Power System Control Centers must operate and control medium and large electrical networks that usually cover a large geographic area
The main advantages of using the modified Hopfield neural network proposed in this work are i-) the internal parameters of the network are explicitly obtained by the valid-subspace technique of solutions, ii-) lack of need for adjustment of penalty factors for initialization of constraints, and iii-) for real time application, the modified Hopfield network offers simplicity of implementation in analog hardware or a neural network processor.(iv) training and testing of the neural network under human supervision is not required
The proposed method is tested on Ward Hale 6 bus system and modified IEEE 30 bus system and the results are compared with conventional and optimization techniques
Summary
Modern Power System Control Centers must operate and control medium and large electrical networks that usually cover a large geographic area. Since the breakers and switches in any substation can cause the network topology to change, a program must be provided that reads the telemeter breaker/switch status indications and restructures the electrical model of the system Every time when a system configuration is changed for some reasons, a topological observability test should be executed prior to performing the state estimation to check one-to-one correspondence between measurements and buses. If this is not the case, observability analysis methods can provide the minimum set of additional measurements needed to restore observability. The main advantages of using the modified Hopfield neural network proposed in this work are i-) the internal parameters of the network are explicitly obtained by the valid-subspace technique of solutions, ii-) lack of need for adjustment of penalty factors for initialization of constraints, and iii-) for real time application, the modified Hopfield network offers simplicity of implementation in analog hardware or a neural network processor.(iv) training and testing of the neural network under human supervision is not required
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