Awan Uji Krismanto

Influence of renewable energy based microgrid on low frequency oscillation of power systems

Increase proliferation of microgrid (MG) technologies with extended use of renewable energy sources (RES) can affect the system stability performance since dynamic characteristics of this kind of MG are different from conventional generation. Sharing certain portion of or entire generated power from synchronous generator with MG results in decrease of total system inertia. On the other hand, connecting RES based MG near a central load eventually enhance system performance due to improvement in reliability, reduction of power loss and congestion on transmission line. This paper investigates the impact of RES based MG on local and inter-area oscillatory modes of power systems. This study focuses on low oscillatory eigenvalues in the frequency range of 0.1-2 Hz. Eigenvalues analysis is performed to observe damping ratios and stability margins of the system due to MG integration. Furthermore, time domain simulation is then carried out to validate the result from eigenvalues analysis.

Influence of BES system on local and inter-area oscillation of power system with high penetration of PV plants

Integration of large-scale photovoltaic power plants is increasing significantly in many develop and developing countries. Although photovoltaic (PV) plants are renewable and environmental friendly, they can bring negative impact in the form of low frequency oscillation due to the uncertainty of power output and negligible inertia. Hence, battery energy storage systems (BESSs) are becoming an inevitable device in power systems. This paper investigates the influence of BESS on small signal stability in multi-machine power system with large-scale PV penetration. To analyze the impact of BESS, proportional gain controller of BESS has been varied. Eigenvalue, participation factor analyses and time domain simulation are implemented to assess the small signal stability of the system. From the simulation results, it is found that BESSs proportional gain controller variation has significant influence in local and inter-area oscillations modes of selected power system.

Dynamic droop control in microgrid for stability enhancement considering RES variation

In this paper, small signal stability analysis of a hybrid Microgrid (MG) considering RES variations is addressed. As wind speed or solar irradiance fluctuates, active power output from DGs might vary significantly. Hence, the power sharing scheme would change considerably. Dynamic droop-gain control is proposed to deal with the RES change and maintain the stability of MG. The proposed control method provides adjustable power sharing strategies to manage RES fluctuation and ensure frequency and voltage regulation of each DGs. Eigenvalues analysis and time domain simulation suggest that at high wind speed and solar irradiance the damping ratio of critical modes and dynamic performance of DG units defer significantly. As the dynamic droop controller implemented, the damping performance and stability margin of the hybrid MG were improved in different operating condition, ensuring stable MG operation in most of RES conditions.

Artificial neural network-based harmonics extraction algorithm for shunt active power filter control

This paper presents a harmonics extraction algorithm using artificial neural network methods. The neural network algorithm was used due to the simpler calculation process compared with conventional method such as fast Fourier transform (FFT). Two types of neural network, i.e., multi-layer perceptron (MLP) and radial basis function (RBF) were employed to extract harmonics current component from its distorted wave current. Further, the extracted harmonics current was used as reference current for shunt active power filter (APF) control. This paper compared the performance of MLP and RBF for harmonics extraction. The advantages of RBF are simpler shape of the network and faster learning speed. Unfortunately, the RBF need to be trained recursively for various harmonics component. MLP can be used to extract various harmonics component in specific data range but need large number of data training hence slower training process.