The current needs of more nonlinear loads and the frequent usage of single-phase loads in three-phase system drastically create power quality issues in the grid-connected system. As a consequence, it creates an undesirable power quality issue (PQI) in the form of a change in the nature of voltage and current magnitude and waveforms in the power system. The voltage-related PQI leads to a huge disturbance in the system when compared with the current-related PQI. The hybrid series active power filter provides grids with the required voltage in series and suppresses the voltage-related harmonics caused by grid-connected nonlinear loads. The present work deals with an adaptive neurofuzzy inference system controller for the generation of a reference voltage signal that uses a reduced active filter rating. The simulation study was done in the MATLAB 2020b/Simulink environment and the experimental effectiveness of the proposed ANFIS controller was compared with that of a conventional controller. In the grid-connected system, this system prevents voltage quality problems such as voltage sag, flickering, voltage swell, neutral currents, and reactive power. The renewable energy sources interfaced into the DC-link minimize short and long voltage challenges so that they improve the overall performance of the system. In accordance with IEEE standard 519-1992, a prototype model was proved to demonstrate that the power delivery system works effectively under different conditions and reduces the total harmonic distortion by approximately 30%, which is less than the 5% acceptable limit.
A significant portion of the Indian population lives in villages, some of which are located in grid-disconnected remote areas. The supply of electricity to these villages is not feasible or cost-effective, but an autonomous integrated hybrid renewable energy system (IHRES) could be a viable alternative. Hence, this study proposed using available renewable energy resources in the study area to provide electricity and freshwater access for five un-electrified grid-disconnected villages in the Odisha state of India. This study concentrated on three different kinds of battery technologies such as lithium-ion (Li-Ion), nickel-iron (Ni-Fe), and lead-acid (LA) along with a diesel generator to maintain an uninterrupted power supply. Six different configurations with two dispatch strategies such as load following (LF) and cycle charging (CC) were modelled using nine metaheuristic algorithms to achieve an optimally configured IHRES in the MATLAB© environment. Initially, these six configurations with LF and CC strategies were evaluated with the load demands of a low-efficiency appliance usage-based scenario, i.e., without demand-side management (DSM). Later, the optimal configuration obtained from the low-efficiency appliance usage-based scenario was further evaluated with LF and CC strategies using the load demands of medium and high-efficiency appliance usage-based scenarios, i.e., with DSM. The results showed that the Ni-Fe battery-based IHRES with LF strategy using the high-efficiency appliance usage-based scenario had a lower life cycle cost of USD 522,945 as compared to other battery-based IHRESs with LF and CC strategies, as well as other efficiency-based scenarios. As compared to the other algorithms used in the study, the suggested Salp Swarm Algorithm demonstrated its fast convergence and robustness effectiveness in determining the global best optimum values. Finally, the sensitivity analysis was performed for the proposed configuration using variable input parameters such as biomass collection rate, interest rate, and diesel prices. The interest rate fluctuations were found to have a substantial impact on the system’s performance.
Forest is one of the main sources of living organisms. Its needs start from the human breath to usage of the wood. But due to many reasons, area occupied by the forest is reducing every year. The reasons behind these environmental impacts are natural disasters (forest fires), deforestation activities, and unlawful actions. Forest fire could be creating most serious threat to wild animals and resources of human welfares. The primary phenomenon of the wild fire occurrence is circumstance hotness of the forest. The dry and hot atmosphere caused the fire in the forest. The deforestation and smuggling activities are also tridents to the available forest. The main consideration of this article is to detect wildfires in advance and protect forest resources from social crimes through advanced sensor integration in the IoT (Internet of Things) environment. A smart forest alert monitoring system has been proposed in this article to avoid forest mishap over by automated self-decision-making protective actions such as parameter measures and alert and implementing the harm mitigation actions related to the hot temperature, humidity, smoke, and smuggling of trees. All the sensors work as per the algorithm designed by the specific application of IoT (Internet of Things). The accurate predictions of the forest fire events and ensuring the forest safety have been tested and verified by a conducted case study on the real forest zone environment.
Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 106 EUR, 4.515 × 106 EUR, and 4.538 × 106 EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 106 EUR, 4.868 × 106 EUR, and 4.885 × 106 EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements.
Concerns about pollution, climate change, limited fossil fuel supplies, and the desire to eliminate energy dependency have sparked a surge in interest in electric vehicles (EVs). EV requirements have resulted in a variety of difficulties and remedies in EV technology. One of them is the use of DC-DC converters to transfer the level of voltage from the battery in an EV to other needed voltage levels. An independent converter for each operating voltage might be used as a remedy. On the other hand, single input multiple output (SIMO) converters can be utilized to decrease costs, reduce switching loss, and thus enhance the system efficiency. In this paper, a nonisolated step-up converter with the integration of the Luo network is proposed for multiple outputs (24 V and 48 V). In electric vehicles, 48 V is utilized for battery backup, while 24 V is utilized for the horns, headlights, telematics, or the microcontroller. The experimental observations of a 36 V, 600 mA, 24 W prototype confirm the theoretic examination and demonstrate the advantages of the proposed converter over other multioutput converters. The STM microcontroller, based on an ARM cortex microprocessor, is linked into the Luo network for making pulses. The proposed converter achieves 94.2% efficiency at full power. The proposed converter’s performance is evaluated through MATLAB/SIMULINK software, and the results are validated experimentally.
Rural electrification is necessary for both the country’s development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study.
Efficient transmission of power is a pressing concern in modern power systems as it could relieve additional investments (e.g., right of way) and may improve stability. Non-uniform loading of transmission lines (which normally occurs due to the inefficient transmission of power) may lead to overloading of a few lines. These lines would then be prone to voltage instability. However, this problem would be aggravated under the network contingency condition. This paper focuses on improving the line loadability of the transmission system by considering the benchmark voltage stability index named rapid voltage stability index. The optimal loadability problem is considered using the grey wolf algorithm. The proposed work is implemented on a standard IEEE 30 bus test system using MATLAB software by addressing the problem by using line stability voltage index and grey wolf algorithm in optimal power flow. Minimizations of cost of generation, carbon emissions, voltage deviation, and line losses have been considered as objectives and improve the line loadability of the transmission system. The simulation results show that the proposed method is very effective in improving line loadability, reducing line congestion and fuel cost. Furthermore, the methodology is tested rigorously under various contingency conditions and is shown to be very effective. The proposed method relieves transmission line congestion and reduces fuel costs using the rapid voltage stability index (RVSI) is tested on an IEEE 30-bus standard test system utilizing MATLAB for various contingency lines
This work relates to the reduction of a noninteger commensurate high dimensional system. The essential objective of this article is to come up with an approximating technique to replace the original high dimensional system with a low dimensional model preserving the properties of the original system in its shortened model. Superiority of the proposed technique is exhibited by correlating the reduced model with the models of other current methods. The simulation results are cited to approve that the recommended technique has high efficiency with a closing value of time-domain specifications. Conclusively, more logical comparisons were made between other existing methods. The performance indices are calculated for both original system and reduced model system and presented in the manuscript.
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