In this work, a chaotic search-based hybrid Sperm Swarm Optimized-Gravitational Search Algorithm (CSSO-GSA) is proposed for automatic load frequency control (ALFC) of a hybrid power system (HPS). The HPS model is developed using multiple power sources (thermal, bio-fuel, and renewable energy (RE)) that generate power to balance the system’s demand. To regulate the frequency of the system, the control parameters of the proportional-integral-derivative (PID) controller for ALFC are obtained by minimizing the integral time absolute error of HPS. The effectiveness of the proposed technique is verified with various combinations of power sources (all sources, thermal with bio-fuel, and thermal with RE) connected into the system. Further, the robustness of the proposed technique is investigated by performing a sensitivity analysis considering load variation and weather intermittency of RE sources in real-time. However, the type of RE source does not have any severe impact on the controller but the uncertainties present in RE power generation required a robust controller. In addition, the effectiveness of the proposed technique is validated with comparative and stability analysis. The results show that the proposed CSSO-GSA strategy outperforms the SSO, GSA, and hybrid SSO-GSA methods in terms of steady-state and transient performance indices. According to the results of frequency control optimization, the main performance indices such as settling time (ST) and integral time absolute error (ITAE) are significantly improved by 60.204% and 40.055% in area 1 and 57.856% and 39.820% in area 2, respectively, with the proposed CSSO-GSA control strategy compared to other existing control methods.

Microgrid tends to be the cluster of some of the renewable energy sources. The most important research area in the power distribution system side is the improvement in the quality of power delivered to the end users. This paper focuses on enhancing the power quality of the microgrid system; shunt active power filters (SAPF) is employed at the distribution side and to design an appropriate controller that achieves a better compensation for the considered SAPF. It is to be noted that the compensation methodology is dependent on the regulation process of the DC-link voltage. Traditionally, this regulation process is carried out employing a closed loop proportional-integral controller. In this paper, a hybrid biogeography-based optimisation – particle swarm optimization-based extreme learning machine neural network model is proposed to design the compensation for the SAPF and to mitigate the harmonics so that effective power gets delivered through the grid.

This study proposes an innovative smart irrigation system designed to address the inefficiencies of traditional methods. Utilizing sensors, microcontrollers, and other integrated components, the system ensures efficient water usage, promoting a more sustainable approach to agriculture. By providing real-time soil moisture monitoring and rainfall data, the system empowers farmers to make informed irrigation decisions, thereby improving crop management. Key features, such as a soil moisture sensor and rain sensor to prevent over-watering, enhance the system's effectiveness in promoting optimal plant growth. Overall, this proposed smart irrigation system demonstrates how technology can be effectively applied to solve real-world agricultural challenges.