Effective virtual inertia control using inverter optimization method in renewable energy generation

Abstract

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

Frequency Stability Enhancement Using Differential-Evolution- and Genetic-Algorithm-Optimized Intelligent Controllers in Multiple Virtual Synchronous Machine Systems

“…In the study, the contribution of DFIG type wind turbines in reducing power system oscillation is analyzed by considering different wind penetration scenarios. In this regard, some of the optimization techniques used by a few of the authors are particle swarm optimization (PSO), multi-objective particle swarm optimization (MOPSO), and distributed grey wolf optimization (DGWO) methods, which are briefly discussed in [29][30][31], respectively. The powerful optimization quality of DE in multi-area applications compared to other metaheuristic techniques, such as those with HVDC and IPFC, has also been clearly discussed in [32,33].…”