Visible to the public Artificial neural network based static security assessment module using PMU measurements for smart grid application

TitleArtificial neural network based static security assessment module using PMU measurements for smart grid application
Publication TypeConference Paper
Year of Publication2016
AuthorsParamathma, M. K., Devaraj, D., Reddy, B. S.
Conference Name2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)
ISBN Number978-1-4673-6725-7
Keywordsartificial neural network, artificial neural network based static security assessment module, Artificial neural networks, Collaboration, genetic algorithm, genetic algorithms, governance, Government, IEEE standards, inter connected power system, neural nets, Neurons, optimization model, phasor measurement, Phasor Measurement Unit, phasor measurement units, PMU measurements, policy, policy-based governance, power engineering computing, power system security, pubcrawl, Real-time Systems, Resiliency, security, smart grid application, smart grid system, smart power grids, standard IEEE 30 bus system, static security assessment scheme, Training, voltage magnitude, Voltage measurement, zero injection buses

Power system security is one of the key issues in the operation of smart grid system. Evaluation of power system security is a big challenge considering all the contingencies, due to huge computational efforts involved. Phasor measurement unit plays a vital role in real time power system monitoring and control. This paper presents static security assessment scheme for large scale inter connected power system with Phasor measurement unit using Artificial Neural Network. Voltage magnitude and phase angle are used as input variables of the ANN. The optimal location of PMU under base case and critical contingency cases are determined using Genetic algorithm. The performance of the proposed optimization model was tested with standard IEEE 30 bus system incorporating zero injection buses and successful results have been obtained.

Citation Keyparamathma_artificial_2016