Assessment of temporary overvoltages during network lines re-energization

Power system blackouts are very infrequent, but they have a Brobdingnagian effect on the system performance and devices. The present research work offers remarkable techniques for the assessment of temporary overvoltages all through the re-energization of network lines. The main goal of this research work is to first-rate and reenergize the network lines for the purpose of restoration. In the later stage, the magnitudes and durations of the Temporary Overvoltages (TOVs) that occurred during the energization of unloaded transformer are estimated.The assortment and re-energization of network lines is done on the basis of Data Envelopment Analysis (DEA) and conceptual method respectively. The assessment of TOVs is done on the basis of MATLAB/Simulink and Feed Forward Neural Networks (FFNNs). The proposed models are verified on IEEE 30 bus test system for the analysis purpose. The Mean Absolute Percentage Error (%MAPE) obtained through various forecasting methods is examined to check the robustness of the proposed approaches. The simulation and FFNN results presented in this research work helps in designing the exact withstand voltage rating for various network components employed at the moment of re-energization

Transmission congestion management considering multiple and optimal capacity DGs

Abstract Transmission congestion management became a grievous issue with the increase of competitiveness in the power systems. Competitiveness arises due to restructuring of the utilities along with the penetration of auxiliary services. The present study depicts a multi objective technique for achieving the optimal capacities of distributed generators (DG) such as solar, wind and biomass in order to relieve congestion in the transmission lines. Objectives like transmission congestion, real power loss, voltages and investment costs are considered to improve the technical and economical performances of the network. Multi objective particle swarm optimization algorithm is utilized to achieve the optimal sizes of unity power factor DG units. The insisted methodology is practiced on IEEE-30 and IEEE-118 bus systems to check the practical feasibility. The results of the proposed approach are compared with the genetic algorithm for both single and multi-objective cases. Results revealed that the intimated method can aid independent system operator to remove the burden from lines in the contingency conditions in an optimal manner along with the improvement in voltages and a reduction in real power losses of the network

Constrained Static/Dynamic Economic Emission Load Dispatch Using Elephant Herd Optimization

The rapid growth in greenhouse gases (GHGs), the lack of electricity production, and an ever-increasing demand for electrical energy requires an optimal reduction in coal-fired thermal generating units (CFTGU) with the aim of minimizing fuel costs and emissions. Previous approaches have been unable to deal with such problems due to the non-convexity of realistic scenarios and confined optimum convergence. Instead, meta-heuristic techniques have gained more attention in order to deal with such constrained static/dynamic economic emission load dispatch (ELD/DEELD) problems, due to their flexibility and derivative-free structures. Hence, in this work, the elephant herd optimization (EHO) technique is proposed in order to solve constrained non-convex static and dynamic ELD problems in the power system. The proposed EHO algorithm is a nature-inspired technique that utilizes a new separation method and elitism strategy in order to retain the diversity of the population and to ensure that the fittest individuals are retained in the next generation. The current approach can be implemented to minimize both the fuel and emission cost functions of the CFTGUs subject to power balance constraints, active power generation limits, and ramp rate limits in the system. Three test systems involving 6, 10, and 40 units were utilized to demonstrate the effectiveness and practical feasibility of the proposed algorithm. Numerical results indicate that the proposed EHO algorithm exhibits better performance in most of the test cases as compared to recent existing algorithms when applied to the static and dynamic ELD issue, demonstrating its superiority and practicability