The determination of the transient stability of an electric power system is a crucial step in power system analysis. This paper investigates the transient stability of an IEEE-9 bus system consisting of three generators and nine buses. At first, a load flow analysis is conducted in order to determine the pre-fault conditions. Secondly, fault analysis is performed to analyze post fault conditions like the fast fault clearing time and load switching in order to determine the system stability. For transient stability analysis, Euler and Runga methods are compared and applied on the frequency and rotor angle of the system to analyze the system variations under different fault conditions. The simulations were done on the Power World Simulator (PWS) software. It is concluded that Critical Fault Clearing Time (CFCT) is a very important factor in keeping the power system within the stability bounds. A slight increase in Clearing Time (CT) from the critical value causes un-synchronism.

Introduction. Nowadays, power quality issues are of considerable interest to both utilities and end users as they cause significant financial losses to the industrial customers. Due to this, power quality assurance in power distribution systems is very important, when considering commercial and industrial applications. Problem Statement. Unfortunately, sudden faults such as sag, transients, harmonics distortion and notching in the power system create disturbances and affect the load voltages. Out of these, voltage sag and harmonics seriously affect sensitive devices. Harmonics in the power system cause increased heating of equipment and conductors, misfires in variable speed drives, and torque pulsations in motors. Harmonics reduction is considered desirable. Methodology. This paper presents an efficient and robust solution to this problem by using dynamic voltage restorer in series with distribution system. Dynamic voltage restorer is economical and effective solution for protecting sensitive loads from harmonics and sag. Control strategy is adopted with dynamic voltage restorer topology and the performance with the proposed controller is analyzed. Novelty. In this research work modelling, analysis and simulation of dynamic voltage restorer with proportional integral controller and dynamic voltage restorer with sliding-mode controller at optimal voltage is used to improve the dynamic voltage restorer performance by reducing total harmonic distortion. Results. The simulation is performed in MATLAB / Simulink software package and comparative analysis of dynamic voltage restorer with different controllers for distribution system is presented. The proposed scheme successfully reduced percentage total harmonics distortion and voltage sag using dynamic voltage restorer with sliding mode controller at optimal voltage which is found to be 0.38 %.

Higher Power Quality (PQ) is a common demand of sensitive industrial customers. PQ issues are gaining attention from both end-users and electrical utility companies since they are generating significant economic losses to sensitive industrial loads. Voltage sags/swells are the most significant and usually occurring PQ issues in a secondary distribution system. Dynamic Voltage Restorer (DVR), is a fast, flexible, effective, and dynamic Custom Power Device (CPD), that can be used to eliminate voltage sags and swells. Its performance is mostly determined by the control strategy established for switching Voltage Source Converters (VSCs). This research work develops a fused control method for VSC of DVR based on the Real-Twisting Algorithm (RTA) and Sliding Mode Control (SMC) that successfully eliminates the impacts of voltage sags/swells. RTA along with the conventional SMC reduce the effect of chattering, which is a disadvantage of SMC while retaining its additional qualities like robustness, quicker response time, and insensitivity to load variations. To evaluate the performance of the proposed control approach, the MATLAB/Simulink SimPower System toolbox was employed. According to the simulation findings, the Real Twisting Sliding Mode Controller (RTSMC) for DVR can detect and mitigate voltage sags/swells within 2.5ms which is much lower than the allowable limit of 20ms as per semiconductor industrial equipment voltage sag immunity standard (SEMI F-47 standard) for sensitive loads. Total Harmonics Distortion (THD) is determined to be less than 5% in all simulated instances. A comparative study is also performed between the conventional SMC and the suggested RTSMC, revealing that the proposed method outperforms the classical SMC.

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

Introduction. Extracting maximum possible power from solar energy is a hot topic of the day as other sources have become costly and lead to pollution. Problem. Dependency on sunlight for power generation makes it unfeasible to extract maximum power. Environmental conditions like shading, partial shading and weak shading are the major aspect due to which the output of photovoltaic systems is greatly affected. Partial shading is the most known issue. Goal. There have been many proposed techniques and algorithms to extract maximum output from solar resources by use of photovoltaic arrays but every technique has had some shortcomings that couldn’t serve the complete purpose. Methodology. Nature inspired algorithms have proven to be good to search global maximum in a partially shaded multipeak curve which includes particle swarm optimization, artificial bee colony algorithm, and flower pollination algorithm. Methods. Particle swarm optimization algorithm is best among these in finding global peaks with less oscillation around maximum power point, less complexity, and easy to implement nature. Particle swarm optimization algorithm has the disadvantage of having a long computational time and converging speed, particularly under strong shading conditions. Originality. In this paper, an improved opposition based particle swarm optimization algorithm is proposed to track the global maximum power point of a solar photovoltaic module. Simulation studies have been carried out in MATLAB/Simulink R2018a. Practical value. Simulation studies have proved that opposition based particle swarm optimization algorithm is more efficient, less complex, more robust, and more flexible and has better convergence speed than particle swarm optimization algorithm, perturb and observe algorithm, hill climbing algorithm, and incremental conductance algorithm.