Electrical and Electronics Engineering

The objective of micro grid (MG) is to generate, regulate and distribute the electricity to the utility grid. Any faults occur in the MG can cause deviations on the voltage, frequency and voltage sag/swell, which affects the power quality. In order to overcome these issues in this paper we proposed to design an adaptive model predictive control (MPC)-based robust extended Kalman filter (REKF) with improved particle swarm optimised selective harmonic elimination (IPSO-SHE) control system. This fetches the importance of power quality improvement. The utilisation of adaptive MPC with robust EKF express the harmonic signatures produced in the system which can be used to detect islanding. An IPSO-SHE is finally employed to reduce the harmonics which significantly improves the quality of power delivered to the load. Our proposed control system has been implemented in the working platform of MATLAB/Simulink and tested with certain load conditions. The simulation results of the proposed control system and comparison with existing control systems shows the significance of the proposed work.

Distributed Generation (DG) units have some desirable features such as environment support, energy
expansion, lower infrastructure costs and deregulation of energy market. DG, which is connected to the grid via
point of common coupling, has many advantages such as peak shaving in order to reduce the overall cost of power
by generating power during peak load hours, and also act as a standby generation to provide electrical power during
outages Due to the availability of parallel connected UPS, increasing the number of DG’s is one of the feasible
solutions to enhance the quality of power. In this paper we propose a novel Model Predictive Control Algorithm
with Extended Kalman Filter (MPC-EKF) are employed for dynamic harmonic state estimation of hybrid DC micro
grid to improve the power quality. The MPC control methodology decomposes the control object into steady-state
and transient sub problems separately and enhances the transient and steady state responses which also identifies the
harmonics present in the system with the help of EKF and after that an Improved Particle Swarm Optimized
Selective harmonic Elimination (IPSO-SHE) is employed by adding adaptive inertia weight to reduce the harmonics
and improve the power quality. Our proposed control system has been designed in MATLAB/SIMULINK and
tested with different load conditions. The simulation results of the proposed control system and comparison with
existing control systems shows the significance of the proposed work in terms of accuracy and faster computational
time.

In this paper we propose to design a Tree Structured
Multi Level Control System based on Model Predictive Control
Algorithm with Extended Kalman Filter (MPC-EKF) to enhance
the quality of power. The proposed MPC-EKF predicts the
non-linear/unbalanced loads by the use of MPC and along with
the prediction there is the need to detect the mode of the grid either
grid connected or islanding mode which can be done with the help
of EKF and further Improved Particle Swarm Optimized Selective
harmonic Elimination (IPSO-SHE) is used to minimise the
harmonics. Our proposed TSML control system has been
developed in the MATLAB and tested with different load
conditions. The simulation results of the proposed TSMLC system
when compared with the existing control systems shows the
impact of the proposed work.

In the past decades, the number of distributed generators (DG) which are
sensitive to adverse environments, the disturbances introduced by the nonlinear
load and proliferation of renewable energy sources are increased considerably.
This fetches the importance of power quality improvement. Also the sudden
changes in the operating conditions of DGs due to load disturbances and
network contingencies produce harmonics. In order to overcome the above
issues in this research work, we proposed to design a novel Model Predictive
Control Algorithm based Tree structured multi-level Control system. The utilization
of Extended Kalman filter with MPC expresses the harmonic signatures
produced in the system which can be used to detect islanding. An Improved
Particle Swarm Optimized Selective harmonic Elimination is finally employed to
reduce the harmonics which significantly improves the quality of power delivered
to the load. Our proposed control system has been implemented in the
working platform of MATLAB/SIMULINK and tested with certain load conditions.
The simulation results of the proposed control system and comparison with
existing control systems show the significance of the proposed work.

To meet the rapidly increasing demand for the electric power generation new methods are evolving in conjunction with the environmental issues. One of such technology is a fuel cell based power generation. Fuel cells are considered as one of the standalone or grid connected distribution generation due to their cleanliness, modularity and higher potential capability.
In the proposed system a three phase boost inverter has been implemented based on a single phase boost inverter topology and is integrated with a fuel cell stack, this eliminates the slow dynamics of the fuel cell and the two stage conversion. By the integration of these the system becomes more efficient and the cost, size and time taken for the response are reduced. The simulink model of three phase boost inverter has been simulated and the simulink results are produced.