Search

The present power generation corporations are working on Renewable Power Systems (RPS) for supplying electrical power to the automotive power industries. There are several categories of RPSs available in the atmosphere. Among all of the RPSs, the most general power network used for Electric Vehicles (EVs) is hydrogen fuel which is available in nature. The H2 fuel is fed to the Proton Exchange Membrane Fuel Stack (PEMFS) for producing electricity for the EV stations. The advantages of this selected fuel system are more power conversion efficiency, environmentally friendly, low carbon emissions, more power density, less starting time, plus able to work at very low-temperature values. However, this fuel stack faces the issue of a nonlinear power density curve. Due to this nonlinear power supply from the fuel stack, the functioning point of the overall network changes from one position of the I–V curve to another position. So, the peak voltage extraction from the fuel stack is not possible. In this article, there are various metaheuristic optimization-based Maximum Power Point Tracking (MPPT) methodologies are studied along with the conventional methods for obtaining the Maximum Power Point (MPP) position of the PEMFS. From the simulative investigation, the Continuous Different Slope Value-based Cuckoo Search Method (CDSV with CSM) provides better efficiency with more output power. Also, for all the MPPT methods comprehensive analysis has been made by utilizing the simulation results. [ABSTRACT FROM AUTHOR]

The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]

Optimal power flow is a complex and highly non-linear problem in which steady-state parameters are needed to find a network’s efficient and economical operation. In addition, the difficulty of the Optimal power flow problem becomes enlarged when new constraints are added, and it is also a challenging task for the power system operator to solve the constrained Optimal power flow problems efficiently. Therefore, this paper presents a constrained composite differential evolution optimization algorithm to search for the optimum solution to Optimal power flow problems. In the last few decades, numerous evolutionary algorithm implementations have emerged due to their superiority in solving Optimal power flow problems while considering various objectives such as cost, emission, power loss, etc. evolutionary algorithms effectively explore the solution space unconstrainedly, often employing the static penalty function approach to address the constraints and find solutions for constrained Optimal power flow problems. It is a drawback that combining evolutionary algorithms and the penalty function approach requires several penalty parameters to search the feasible space and discard the infeasible solutions. The proposed a constrained composite differential evolution algorithm combines two effective constraint handling techniques, such as feasibility rule and ɛ constraint methods, to search in the feasible space. The proposed approaches are recognized on IEEE 30, 57, and 118-bus standard test systems considering 16 study events of single and multi-objective optimization functions. Ultimately, simulation results are examined and compared with the many recently published techniques of Optimal power flow solutions owing to show the usefulness and performance of the proposed a constrained composite differential evolution algorithm. [ABSTRACT FROM AUTHOR]

The potential of solid waste as an energy source is clear, owing to its wide availability and renewable properties, which provide a critical answer for energy security. This can be especially effective in reducing the environmental impact of fossil fuels. Countries that rely heavily on coal should examine alternatives such as electricity from solid waste to provide a constant energy supply while also contributing to atmospheric restoration. In this regards, Low Emissions Analysis Platform (LEAP) is used for simulation the entire energy system in Pakistan and forecasted its capital cost and future CO2 emissions in relation to the use of renewable and fossil fuel resources under the different growth rates of solid waste projects like 20%, 30% and 40% for the study period 2023–2053. The results revealed that, 1402.97 TWh units of energy are generated to meet the total energy demand of 1193.93 TWh until 2053. The share of solid waste based electricity in total energy mix is increasing from a mere 0.81% in 2023 to around 9.44% by 2053 under the 20% growth rate, which then increase to 39.67% by 2053 under the 30% growth rate and further increases to 78.33% by 2053 under the 40% growth rate. It is suggested that 40% growth rate for solid waste based electricity projects is suitable for Pakistan until 2053 because under this condition, renewable sources contributes 95.2% and fossil fuels contributed 4.47% in the total energy mix of Pakistan. Hence, CO2 emissions are reduced from 148.26 million metric tons to 35.46 million metric tons until 2053 but capital cost is increased from 13.23 b$ in 2023 to 363.11 b$ by 2053. [ABSTRACT FROM AUTHOR]

Modern agriculture increasingly relies on technology to enhance productivity and sustainability. This paper explores the integration of wireless sensor nodes as a pioneering approach for smart fault detection in agricultural systems. This research delves into the design, implementation, and validation of a network of wireless sensors strategically placed across agricultural fields. These sensors are equipped with advanced data collection capabilities to monitor various environmental parameters such as soil moisture, temperature, humidity, and plant health indicators. Using machine learning algorithms and data analytics, these sensor nodes autonomously detect anomalies, diseases, irrigation issues, and other faults in real-time. The paper discusses the technological framework, the challenges encountered, and the potential benefits of employing wireless sensor nodes for proactive fault detection in agriculture. The results demonstrate the efficiency of this approach in optimizing irrigation, fertilizer use, predictive harvesting, mitigating crop losses, and fostering sustainable farming practices. Ultimately, this research contributes to the advancement of precision agriculture by offering a scalable and efficient solution for early fault detection and intervention, thereby revolutionizing farming practices towards increased efficiency and sustainability. [ABSTRACT FROM AUTHOR]

This paper concerns a three-phase self-excited induction generator used for autonomous power generation. It presents a robust control strategy which makes it possible to maintain the frequency quasi constant during the voltage regulation without any control loop on this variable. This strategy, which also prevents the machine disengagement, uses as power converter a simple dimmer. The obtained theoretical and/or numerical results are validated on a laboratory test bench that allows the analysis of this control law effectiveness. [ABSTRACT FROM AUTHOR]