S. N. Dhurvey, V.K. Chandrakar



Performance Comparison of PI and MFFN Based IPFC on Damping of Power System Oscillations

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FACTS, IPFC, MFFN, Damping of oscillations.


Neural network learning is a type of supervised learning, meaning that we provide the network with example inputs and the correct answer for that input. This paper discusses a new approach for determining the effective control signals for damping of oscillations by using MFFN (Multilayer feed forward network) based Interline Power Flow Controller [IPFC]. The IPFC performance is tested with PI controllers in comparison with MFFN based controller on Modified Phllips-Heffron Model of Single Machine Infinite Bus system to achieve improved damping performance by selecting effective control signals such as deviation in pulse width
modulation index of voltage series converter 1 in line 1, pulse width modulation index of voltage series converter 2 in line 2, deviation in phase angle of the injected voltage of convertor 1, injected voltage phase angle deviation of convertor 2. Investigations has been found that coordinated tuning of Interline Power Flow Controller with MFFN controller provide the robust dynamic performance. The MFFN Based Interline Power Flow Controller [IPFC] is designed with simple strategy to coordinate the additional damping signal. The proposed controllers for IPFC are able to achieve improved designed performance of the power system. Validity of effective control signals has been done by eigen value analysis.

Cite this paper

S. N. Dhurvey, V.K. Chandrakar. (2016) Performance Comparison of PI and MFFN Based IPFC on Damping of Power System Oscillations. International Journal of Power Systems, 1, 17-26