Enhanced Non-linear PID-Based on Virtual Inertia Control to Enhance the Frequency Stability of a Hybrid Power System

Document Type : Original Article

Authors

1 Department of Electric power and machines, Faculty of Engineering, Helwan University, Cairo, Egypt.

2 Power Dept. , Faculty of Engineering , Helwan University

3 Department of Electric power and machines, Faculty of Engineering, Helwan University, Cairo, Egypt

4 Higher Engineering Institute, Thebes Academy, Cairo, Egypt, (on leave from Helwan University)

Abstract

The loss of system inertia caused by the replacement of conventional generating units with a high number of renewable energy sources (RESs) has an unfavorable impact on the power system's frequency stability, resulting in the power system's weakening. A hybrid power system with low inertia has a real challenge to maintaining frequency stability under different operating conditions. To enhance the single area hybrid power system's frequency stability and save it from blackout, this paper introduces Enhanced Non-linear PID (ENLPID) based on virtual inertia control (VIC) for a single area hybrid power system with a high contribution of RESs. The ENLPID based on VIC has a great effect in enhancing the frequency stability of the studied hybrid power system. This effect is remarkable in handling the different contingency conditions. To improve this enhancement in the frequency stability of the studied single area hybrid power system, the ENLPID based on VIC response is compared to optimal non-linear PID (NLPID) based on VIC, optimal PID based on VIC, and conventional VIC (CVIC) responses. Moreover, these responses of control techniques based on VIC are compared with the response of the studied single area hybrid power system without VIC by using MATLAB TM/Simulink. This comparison shows the effect of using the VIC concept in saving the hybrid power system stability and the role of ENLPID in improving the frequency response of the system. The optimal parameters of the ENLPID based on VIC are obtained by using the particle swarm optimization (PSO) technique.

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