Multi-objective Grasshopper Optimization Research Articles

A new molten carbonate fuel cell hybrid power generation system using two-stage sodium thermo-electrochemical converter/two-stage thermoelectric generator: Performance analysis and multi-objective grasshopper optimization

To enhance the poor power density of the molten carbonate fuel cell, a novel combination of two-stage sodium thermo-electrochemical converter and a two-stage thermoelectric generator is introduced to recover the heat waste of the molten carbonate fuel cell for further power generation. The performance criteria of the hybrid system in terms of power outputs, efficiencies, exergy destruction, and cost rate are mathematically derived and analyzed using energy, exergy, and exergoeconomic methods. Finally, the grasshopper multi-objective optimizer and decision-making procedures are applied to find the maximum power density and exergy efficiency for the hybrid system with the minimum power density cost rate. It is found that the preferable optimum operation of the system provides the highest power density of 2652.1 W/m2 with 53.96% and 57.58% energetic and exergetic efficiencies which are 42.28% and 38.89% larger than the individual molten carbonate fuel cell, respectively. Moreover, the minimum power density cost rate is calculated by 1.13 $/h m2, indicating a 26.14% improvement compared to the design condition and a 0.51 $/h m2 increase relative to the sole molten carbonate fuel cell. The results suggest that the present system is a favorable alternative to other hybrid systems.

A Novel Wind Speed Interval Prediction System Based on Neural Network and Multi-objective Grasshopper Optimization

As a clean energy source, the role of wind power in the energy mix is becoming increasingly important. Reliable and high-quality wind speed prediction results are key to wind energy utilization. The traditional point prediction method cannot effectively analyze the uncertainty of wind speed, and the interval prediction model can provide the possible variation range of wind speed under a certain confidence probability and supply more uncertain information to decision makers. However, the previous interval prediction models generally ignore the random characteristics of capturing wind speed and the importance of objective selection of prediction submodels, leading to poor prediction results. To address these problems, a combined model based on data preprocessing, multi-neural network models, multi-objective optimization, and an improved interval prediction method is proposed. The model is applied to five wind speed forecasting examples in Dalian to test the prediction accuracy, multi-step prediction ability, and universality and generalization ability of the model. The experimental results show that the model proposed in this study is satisfactory for various performance evaluation indexes, has high stability and accuracy, and all the solutions obtained by the model are Pareto optimal solutions. Thus, it provides a reliable reference for the effective utilization of wind energy.

Multi-Objective Grasshopper Optimization Based MPPT and VSC Control of Grid-Tied PV-Battery System

This article presents the control of a three-phase three-wire (3P-3W) dual-stage grid-tied PV-battery storage system using a multi-objective grass-hopper optimization (MOGHO) algorithm. The voltage source converter (VSC) control of the presented system is implemented with adaptive kernel width sixth-order maximum correntropy criteria (AKWSOMCC) and maximum power point tracking (MPPT) control is accomplished using the variable step-size incremental conductance (VSS-InC) technique. The proposed VSC control offers lower mean square error and better accuracy, convergence rate and speed as compared to peer adaptive algorithms, i.e., least mean square (LMS), least mean fourth (LMF), maximum correntropy criteria (MCC), etc. The adaptive Gaussian kernel width is a function of the error signal, which changes to accommodate and filter Gaussian and non-Gaussian noise signals in each iteration. The VSS-InC based MPPT is provided with a MOGHO based modulation factor for better and faster tracking of the maximum power point during changing solar irradiation. Similarly, an optimized gain conventional PI controller regulates the DC bus to improve the power quality, and DC link stability during dynamic conditions. The optimized DC-link generates an accurate loss component of current, which further improves the VSC capability of fundamental load current component extraction. The VSC is designed to perform multi-functional operations, i.e., harmonics elimination, reactive power compensation, load balancing and power balancing at point of common coupling during diverse dynamic conditions. The MOSHO based VSS-InC, and DC bus performance is compared to particle swarm optimization (PSO) and genetic algorithm (GA). The proposed system operates satisfactorily as per IEEE519 standards in the MATLAB simulation environment.