Optimal fractional sliding mode control for the frequency stability of a hybrid industrial microgrid

The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions

Detection of islanding and fault disturbances in microgrid using wavelet packet transform

Fast detection of islanding is very important for effective operation and control in distributed generation (DG) penetrated distribution networks. The islanding detection techniques such as passive, active, communication, and hybrid have their own merits and demerits. This paper proposed wavelet transform (WT) and wavelet packet transform (WPT) based techniques for detection of islanding and fault disturbances in a microgrid consisting of resources like wind turbine generator, fuel cell (FC), and microturbine. Voltage signal is extracted at the point of common coupling (PCC) and is passed through these detection techniques to obtain the time-frequency multi-resolution analysis. Further, to validate the graphical study, performance indices (PIs) like standard deviation and entropy are calculated for the disturbance detection using suitable selection of threshold. A comparative analysis using WT and WPT is presented in the form of graphical simulation as well as in terms of PIs to analyse their effectiveness and robustness under different operating conditions. It is observed that WPT shows better detection capability in comparison to WT even under 20-dB noisy scenarios.National Research Foundation (NRF)Accepted versionAuthors acknowledge the support for research from the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme