Shaik, Abdul Gafoor

The synchronization of the Doubly-Fed Induction Generator (DFIG) into the weak AC grid is significantly affected due to the reactive power limitations associated with the built-in converter of the existing Wind Energy (WE) system. This is further restricted due to non-linear loads and Power Quality (PQ) deterioration. Therefore, there is a strong need for a system capable of managing reactive power without disturbing the existing infrastructure of WE sources. This paper proposes a novel reactive power-based solution for synchronizing the DFIG to the weak grid with high WE penetration by mitigating PQ disturbances and managing reactive power at the interconnection point using an additional Distribution-Static Compensator (DSTATCOM). A feed-forward second-order-based adaptive algorithm controls this reactive power compensator. The adaptive control extracts the active and reactive weights of load current to generate the reference signals by continuously tracking grid voltage, wind generation, and DC-link voltage. The proposed novel approach has been successfully implemented in MATLAB and validated with a laboratory-based experimental setup. The transient and steady-state analysis reveals the practical applicability of a proposed novel synchronization approach with a high WE penetration level.

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of African Vulture Optimization Algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (all sources), with significant reduction of 5.25% and 33.09% in cost (323318.21$/day) and emission (of 2433.95 Tons/day) respectively compared to the closest competitive method.

Power quality issues in weak grids with large impedance pose significant challenges that limit wind energy (WE) penetration levels and the performance efficiency of existing WE infrastructure. The presence of non-linear (NL) loads at the point of common coupling (PCC) further restricts these levels. This paper addresses these challenges by introducing an additional distributed static compensator (DSTATCOM) at the PCC, controlled by a higher-order Delayed Least Mean Fourth (DLMF) algorithm. The proposed DLMF control algorithm estimates the active and reactive components of the load current by updating their respective weights with appropriate delays, considering variations in loads, DC-link voltage, and wind energy generation. The MATLAB implementation of the proposed control is designed and validated through experimental investigations. These investigations involve varying short circuit ratios, wind speeds, and the presence of NL loads. The results demonstrate that the proposed method can enhance wind penetration levels in weak grids by up to 30%.

This article contributes to solve all day Economic Emission Load Dispatch (EELD) problem of a wind integrated power system specifically based on field data of Jaisalmer, Rajasthan, India using Search and Rescue method in stochastic approach. The proposed S-SAR method utilizes a stochastic technique to account for the inherent uncertainty of wind. It employs the weibull probability density function (pdf) to represent wind speed and conducts 1000 Monte Carlo simulations to capture the uncertainty in the frequency distribution model of wind power. The SAR method has also been employed to solve the EELD problem using a conventional/deterministic methodology to facilitate a comparative analysis of its performance in comparison to a stochastic approach. In addition, the performance of S-SAR is compared to four established algorithms, namely Whale Optimization Algorithm, Grey Wolf Optimization, Moth Flame Optimization, and Particle Swarm Optimization, in order to demonstrate its superiority. The proposed approach is tested using criteria such as optimal generation cost, emission, power deviation factor, simulation time, convergence curves, etc. Results are statistically validated by analyzing the statistical metrics of optimum cost from 1000 monte carlo simulations. The efficacy of the proposed approach to tackle EELD at different levels of wind penetration is further evaluated through a supplementary case study conducted on the system. The simulation findings demonstrate the effectiveness of the suggested S-SAR technique in addressing wind uncertainty, with a 2.46% decrease in cost and a 4.08% decrease in emissions compared to the next competitive option being studied.