ADVANCED FREQUENCY CONTROL IN MICROGRID-CONNECTED HYBRID POWER SYSTEMS VIA SMES-BASED NEURAL NETWORK CONTROLLER OPTIMIZED WITH SINE COSINE ALGORITHM
M. Mujtahid Ansari, Prashant J. Gaidhane
Abstract: This paper focuses on frequency regulation in a microgrid connected hybrid power system incorporating renewable energy sources i.e. solar PV generation and wind power generation, diesel engine generator (DEG) and superconducting magnetic energy storage (SMES) unit. Renewable intermittency and uncontrolled load change causes large frequency deviation and requires fast and adaptive control supports. In order to overcome this issue, Sine Cosine Optimized Neural Network Controller (SCONN) is proposed for dynamic SMES power modulation and DEG is used for sustained secondary frequency regulation. The proposed controller parameters are optimized by the Sine Cosine Optimization algorithm to provide better damping and less during transient oscillations and under different operating conditions. MATLAB/Simulink simulations are performed in the case of step and continuous load disturbance. Results show that with the SCONN controller the maximum frequency deviation is mitigated down to 0.189 Hz from 0.323 Hz (PID) with 62.1% reduction in ITSE as well as with 39.7% reduction in overshoot. Compared to the PI controller the maximum deviation improvement is 54.8%. The obtained responses confirm that a SMES sustained by SCONN control helps to significantly increase frequency stability in hybrid microgrid function.
Keywords: Diesel-engine generator; Neural network; Photovoltaic; Sine cosine optimization; Superconducting magnetic energy storage
DOI: 10.24874/PES08.03.013
Recieved: 02.06.2026 Revised: 28.07.2026 Accepted: 26.08.2026
UDC:
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