Harmonic Profile Enhancement of Grid Connected Fuel Cell through Cascaded H-Bridge Multi-Level Inverter and Improved Squirrel Search Optimization Technique

Abstract

The generation of energy by conventional systems leads to several environmental issues. Fuel Cell (FC), being a new renewable energy source, has emerged as one of the promising alternatives to obtain clean and efficient energy generation. This paper highlights the power quality enhancement of the grid connected FC through a boost converter and 25 level Cascaded H-Bridge (CHB) Multi-Level Inverter (MLI) using the classical PID controller. To drive the MLI connected to the grid for governing the Point of Common Coupling (PCC) voltage between the FC and the grid, two PID controllers have been utilized. The conventional evolutionary techniques such as Particle Swarm Optimization (PSO) and Squirrel Search Algorithm (SSA) are implemented to tune the PID controllers for dynamic operations. To further enhance the convergence speed of computation and precision of the classical techniques used, an Improved Squirrel Search Algorithm (ISSA) has been proposed in this work. The grid connected power network considered for study here is designed using MATLAB/Simulink environment. Moreover, the system is led to various rigorous voltage sag and swell conditions to test the effectiveness of the proposed controller. A detailed comparison between the conventional PID, PSO, SSA, and proposed ISSA techniques in voltage profile improvement, power quality enhancement, and reduced execution time has been featured. The results obtained highlight the proposed technique’s superiority over the classical methods in terms of improved dynamic voltage response, enhanced power quality, and reduced harmonics. The power quality indices are found out using Total Harmonic Distortion (THD) analysis. The values found out are well within the IEEE-547 indices for the proposed controller, thus justifying its real-time implementation.

A Unified Linear Self-Regulating Method for Active/Reactive Sustainable Energy Management System in Fuel-Cell Connected Utility Network

“…The comparison is made using five different criteria, including the reduction of THD in the four case studies covered in the previous sections IV-A and IV-B, the type of SEM used in synchronization strategy, power outage performance, and the complexity of the system. The SEM suggested in [38] has the closest performance. However, compared to the other FC system in this comparison, this system is the most costly and complicated due to the usage of 25-level CHB MLI, which requires 12 independent DC sources and transformers.…”

“…Most of the FC systems utilized conventional PLL for synchronization purposes which has many shortcomings during grid voltage oddity such as phase delay, unable to remove the harmonic component, and so on. However, [38] and [50] utilized PID, and Kalman and fuzzy integrated controllers, respectively, for synchronization purposes. Nonetheless, Kalman and fuzzy integrated controllers introduce a higher computation burden and make the overall SEM complex.…”

“…Nonetheless, Kalman and fuzzy integrated controllers introduce a higher computation burden and make the overall SEM complex. In terms of power outage performance which is a very important scenario, especially for the critical load condition, only one of the studies [38] considered 20% voltage reduction and increase, however, the power outage performance is not covered on any FC SEMs.…”

“…The use of a similar strategy is shown in [36]. The fact that other FC systems were not evaluated under power outage conditions, except for the FC system produced in [25], [38], [39], and [40] which considered voltage imbalance condi- tions, is a significant finding from the literature study that needs to be addressed. Since voltage imbalance power quality issues especially power outage issues can greatly affect the overall power quality, the power factor, and efficiency of the fuel-cell connected power network, ignoring it creates great instability in the system.…”

Robust Modified Flower Pollination Algorithm for Power Quality Enhancement in an Autonomous 31-Level Cascaded H-Bridge Photovoltaic Inverter with Partial Shading Conditions

“…However, nature-inspired optimization techniques can automatically respond to system parameters changes without physically changing the control parameters. In the recent past, there are many evolutionary techniques available in the literature as suggested by many authors, such as Particle Swarm Optimization (PSO) [16], Weighted Superposition Attraction Algorithm (WSAA) [17], Bee Colony Optimization (BCO) [18], Brain Storming Optimization (BSO) [19], Spider Monkey Algorithm (SMA) [20], Crow Search (CS) [21], Flower Pollination Algorithm (FPA) [22], etc.…”