Banaja Mohanty

Recently fuel cell becomes more popular as renewable energy source to produce electrical energy using hydrogen gas. Optimal modelling of fuel cell is important to extract specified power. In this paper precise calibration of proton exchange membrane fuel cell (PEMFC) is analyzed using optimization process. Hybrid grey wolf optimization‐cuckoo search (hGWO‐CS) algorithm is employed to minimize squared error between measured and simulated terminal voltage. Performance of the algorithm is analyzed for six different bench mark functions. Different optimization algorithms and published results are compared in this paper for Ballad Mark V50 KW, BCS 500 W PEMFC, and NedStack PS6 stacks fuel cell. Among these algorithms with hGWO‐CS lower SSD is achieved for three cases. The effectiveness of the proposed method is also validated comparing theoretical and experimental simulations. Further, computational time and statistical indices like mean, minimum, standard deviation, maximum value RMSE, MAE of SSD for hybrid method specify smallest value amongst all other algorithms which clarifies hybrid method as more robust and effective. Moreover, convergence curves and non‐parametric test additionally confirm sturdiness and consistency of hGWO‐CS in detecting unidentified parameters of PEMFC. Sensitive analysis with variation in optimized parameters are performed to provide insights for optimizing the PEMFC performance. Major contribution of the paper is to design hGWO‐CS algorithm accurately optimizing PEMFC parameter which can improve overall performance of fuel cell.

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF₁, ΔF_2, and ΔP_Tie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach.

The fuel cell is vital in electrical distribution networks as a distributed generation in today’s world. A precise model of a fuel cell is extensively required as it rigorously affects the simulation studies’ transient and dynamic analyses of the fuel cell. This appears in several microgrids and smart grid systems. This paper introduces a novel attempt to optimally determine all unknown factors of the polymer exchange membrane (PEM) fuel cell model using a meta-heuristic algorithm termed the Lightning search algorithm (LSA). In this model, the current–voltage relationship is heavily nonlinear, including several unknown factors because of the shortage of fuel cell data from the manufacturer’s side. This issue can be treated as an optimization problem, and LSA is applied to detect its ability to solve this problem accurately. The objective function is the sum of the squared error between the estimated output voltage and the measured output voltage of the fuel cell. The constraints of the optimization problem involve the factors range (lower and upper limit). The LSA is utilized in minimizing the objective function. The effectiveness of the LSA-PEM fuel cell model is extensively verified using the simulation results performed under different operating conditions. The simulation results of the proposed model are compared with the measured results of three commercial fuel cells, such as Ballard Mark V 5 kW, BCS 500 W and Nedstack PS6 6 kW, to obtain a realistic study. The results of the proposed algorithm are also compared with different optimized models to validate the model and, further, to determine where LSA stands in terms of precision. In this regard, the proposed model can yield a lower SSE by more than 5% in some cases and high performance of the LSA-PEMFC model. With the results obtained, it can be concluded that LSA prevails as a potential optimization algorithm to develop a precise PEM fuel cell model.

Microgrid is nothing but a cluster of interconnected loads and Distributed Energy Resources (DERs), which operate within a clearly defined small electrical boundary. In this paper, a PV-Fuel cell (FC)- Battery energy stored system (BESS) based Microgrid is selected considering individually their advantages and convenience. This paper is completely based upon design and simulation of Microgrid and dealt with one of the technical challenges i.e. frequency and voltage control in islanded mode of operation. Traditional proportional-integral (P-I) controllers are implemented to control the converter (Voltage Source Converter) at the ac grid. The control design uses a droop controller, with the current and voltage control loop for controlling the frequency and voltage. Also, Grasshopper Optimization Algorithm is used to optimize the control parameters.

Summary: In the fast developing electric power system network, ancillary services like automatic generation control (AGC) plays a vital and significant role to ensure good quality of power supply in the system. To distribute good quality of power, a hybrid AGC system along with an efficient and intelligent controller is compelled. So, in this article, a cascaded proportional‐integral (PI)‐proportional‐derivative (PD) controller with filter (PI‐PDF) is proposed as secondary controller for AGC system. A nature inspired optimization algorithm named as moth flame optimization (MFO) algorithm is employed for simultaneous optimization of controller gains. Initially, a two‐area interconnected nonreheat thermal power system is investigated. Analysis revealed that MFO‐tuned PI controller performs better than the different optimization techniques tuned PI controller for the same system and PI‐PDF controller performs better than PI controller does. Then, the study is extended to a three‐area interconnected hybrid system with proper generation rate constraint. Area‐1 consists of solar thermal‐thermal unit; area‐2 consists of thermal‐hydro unit and area‐3 thermal‐gas unit as generating sources. Performance of PI‐PDF controller is compared with classical controllers such as PI, PID, PIDF, and PI‐PD controller without filter. Result analysis divulges that MFO‐tuned PI‐PDF controller performs better than all other controllers considered in this article. Robustness of the PI‐PDF controller is evaluated using parameter variations and random load variation.