Dynamic Economic Load Dispatch of Hydrothermal System

A Quasi Oppositional Gray Wolf Optimization (QOGWO) algorithm has been used in this work to decipher the economic load dispatch of hydrothermal system. Dynamic economic load dispatch problem involves scheduling of committed generators to meet the load demand with minimum fuel cost and several constraints which are dynamic in nature. It is basically short-term hydrothermal scheduling (STHS) problems through cascaded reservoirs. Instead of pseudo-random numbers quasi-opposite numbers are used to initialize population in the proposed QOGWO method so that the convergence rate of GWO increases. The viability of the projected approach is verified in three standard multi-chain cascaded hydrothermal systems with four interconnected hydro systems. The load and number of thermal units differ from one system to another. Water transportation delay between interconnected reservoirs, Valve Point Loading (VPL) have been considered in different combination in three cases. The technique put forth with established superior to many recent findings for the STHS problems with increased complexities

Performance Evaluation of BB-QZSI-Based DSTATCOM Under Dynamic Load Condition

This paper presents the shunt compensation performance of quasi-Z-source inverter (QZSI) and back to back connected QZSIs (BB-QZSI) to address the power quality (PQ) issues in the three-phase three-wire power utility network (PUN). Generally, these PQ issues are poor voltage regulation, low power factor (PF), source current distortion, unbalanced voltage, etc. The proposed BB-QZSI-based distribution static compensator (DSTATCOM) consists of two QZSIs with a common dc-link capacitor. Because the QZSI could achieve buck/boost conversion as well as DC to AC inversion in a single-stage and the back to back configuration decreases the system down time cost (if a fault occurs in one QZSI the other can continue the shunt compensation). Particularly, icosφ control algorithmcontrol algorithm is implemented to generate proper switching pulses for the switches of DSTATCOM. The effectiveness of the BB-QZSI is verified through simulation studies over QZSI using MATLAB/Simulink software satisfying the recommended grid code

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation