This paper presents the optimization of fuel cost, emission of NO_X, CO_X, and SO_X gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

The increased use of solid state devices at the consumer end has introduced a power quality issue in the form of harmonic distortion due to their nonlinear nature. The magnitude of this harmonic distortion varies with the nonlinear load distribution at various voltage levels of a system. This paper presents a detailed analysis of harmonic distortion in terms of THD for various load combinations in a power system. The trends in THD variations are obtained at various operating conditions. The effect of introducing nonlinear loads at various voltage levels of a system is discussed. The effect of harmonic model of nonlinear load in terms of variation of THD at different buses and the degradation effects of these harmonic distortions are also presented.

Energy harvesting from renewable energy sources is trending in the world due to inventions in modern technology. This paper proposes a grid-tied single-phase modified W-type 81-level inverter. Inverter design equations to calculate various parameters, such as the number of voltage levels and the number of DC power sources, along with the feedback controller equations, are developed to integrate the proposed topology with an electric power grid. The modeling of the control system for the proposed topology is carried out in the synchronously rotating reference frame for single-phase systems. The PWM generation part of the proposed inverter system makes use of the binary search nearest level algorithm to efficiently track the grid voltage signal. The proposed system integrates the inverter with the grid without the need for an output filter. The efficiency analysis shows that the proposed system delivers active and reactive power to the grid with an efficiency of around 90% and a THD of 1.04%. The voltage and current waveforms for the dynamic active and reactive power flow reveal that the proposed system exhibits a good transient and steady-state response. The overall system is simulated in MATLAB/Simulink and the results are verified using a hardware implementation of the prototype circuit.

Induction motors are used in industry in variety of applications such as blowers, fans, compressors, pumps, mixers, crushers and grinders etc. Soft starters, involving voltage controllers, are used for starting and speed adjustment of induction motors. This paper presents a neural network based soft starter which controls the speed by the adjustment of firing angles of thyristors. The suggested technique eliminates the starting torque pulsations by triggering the back to back connected thyristors. The control strategy is implemented using microcontrollers and neural networks and performance analysis of the system is carried out with the help of hybrid model of induction motor. The results obtained are satisfactory and promising. The advantage of such a soft starter is its simplicity, stability and high accuracy as compared to the conventional soft starter which uses mathematical calculations of firing angle which is a complex and time consuming task especially in on-line control applications.

The model predictive control (MPC) technique for a solar‐based series‐resonant inverter conventionally used in domestic heating is explored. The photovoltaic energy system (PVES) converts the solar energy into electrical energy where DC voltages of different cells are linked at a common bus. A Cuk‐converter is used to interface the PVES with the DC load bus and the heating inverter. These heating inverters are replacing gas cookers in restaurants, guest rooms, and domestic‐ and commercial‐kitchens for cooking of various foods. A number of control algorithms have been employed to control the power of the heating inverter but they have certain limitations; especially they do not predict the future response of the circuit. The MPC, an advanced control approach, has the potential to estimate the future behaviour of the model in a predictive manner. Hence, to control the power of the resonant inverter connected with a 3.36 kW heating load, MPC is applied. The simulation results verify the validity of the power control of a solar‐based series‐resonant inverter using the MPC algorithm.

The design analysis of a multi‐input converter using an intelligent controller based on fuzzy logic control algorithm is encompassed. The use of dedicated dc–dc converters is going through a transitory phase in smart‐grid applications. Different characteristics of inputs can be combined to give desired output operation by using a multi‐input converter which is more or less the combination of individual converters sharing a common load thus simultaneously transferring power to the load. The converter with the help of an intelligent algorithm will ensure the buffering time for each input thus multiplexing between various inputs according to the set demand and fluctuating conditions on the input side. Such a converter will behave as a power electronic interface between the utility, user and the renewable energy sources. A three‐input cuk converter has been designed for interfacing wind energy sources with the dc load bus and the main grid. Sending power to the main grid incorporates the power quality enhancement features which will be accommodated using the bi‐directional inverter connected with the grid to perform the active filtration. Moreover, three fuzzy logic controllers will set the output currents corresponding to each input current by changing their reference voltages according to the power demand.