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Reactualization of a Modified Single Machine to Infinite Bus Model to Multimachine System Steady State Stability Analysis Studies Using Losses Network Concepts and Radial Basis Function Neural Network (RBFNN)

Rusilawati Rusilawati(1*), Adi Soeprijanto(2), Rony Seto Wibowo(3)

(1) Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
(2) Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
(3) Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember Surabaya, Indonesia
(*) Corresponding author


DOI: https://doi.org/10.15866/iremos.v10i2.11207

Abstract


To analyze the stability of each generator, the multimachine system must be changed into a Modified Single Machine to Infinite Bus (M-SMIB ) system.  This paper proposes a method to change the multimachine system into a M-SMIB system with an equivalent impedance and an equivalent load by determining the contribution of each generator to a loading value  using a simple technique. After the M-SMIB system is obtained, a steady-state stability analysis can be performed on each generator unit. In this paper, the maximum generation value which is the generator steady state stability limit, is determined. The Radial Basis Function Neural Network (RBFNN) is applied to determine the range area of generator steady state stability with the variable load to observe the influence of load variation in the generator steady state stability area. Thus, the area of safe operation of the generator can be directly observed without causing instability of the system. Therefore, the steady state stability limits of each generator unit can be directly determined every time there is a change of loading and recognize the operation limit of each generator during disturbances occurring on the bus. This method will be applied to a 4-bus IEEE system with 2 generators.
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Keywords


Generator Steady State Stability Limit; Modified Single Machine to Infinite Bus (M-SMIB) System; Radial Basis Function Neural Network (RBFNN)

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