In this paper, the Levy flight-based chicken swarm optimization (LFCSO) is proposed to follow the highest power of grid-joined photovoltaic (PV) framework. To analyze the grid-associated PV framework, the characteristics of current, power, voltage, and irradiance are determined. Because of the low yield voltage of the source PV, a big advance up converter with big productivity is required when the source PV is associated with the matrix power. A tale great advance up converter dependent on the exchanged capacitor and inductor is illustrated in this paper. The LFCSO algorithm with the adaptive neuro-fuzzy inference system is used to generate the control pulses of the transformer-coupled inductor DC–DC converter-less switched capacitor. While using the switched capacitor-coupled inductor, the voltage addition is expanded in the DC–DC converter and the power of PV is maximized. Here, the normal CSO algorithm is updated with the help of Levy flight functions to generate optimal results. To get the accurate optimal results, the output of the proposed LFCSO algorithm is given as the input of the ANFIS technique. After that, the optimal results are generated and they provide the pulses for the system. The working guideline is analyzed and the voltage addition is derived with the utilization of the proposed technique. From that point forward, it predicts the exact maximum power of the converter according to its inputs. Under the variety of solar irradiance and partial shading conditions (PSCs), the PV system is tested and its characteristics are analyzed in different time instants. The proposed LFCSO with ANFIS method is actualized in Simulink/MATLABstage, and the tracking executing is examined with a traditional method such as genetic algorithm (GA), perturb and observe (P&O) technique–neuro-fuzzy controller (NFC) and fuzzy logic controller (FLC) technique.
Accurate short-term load forecasting (STLF) is essential for the efficient operation of the power sector. Due to heightened volatility and intrinsic stochasticity, forecasting load at a fine resolution, such as weekly load, is difficult. Existing STLF techniques only rely on temporal data and auto-regressive processes to forecast load. However, the power grid has a graphical structure that provides spatial information too. This paper proposes an innovative STLF method fusing both spatial and temporal information. We propose a creative way to convert load data into graphical form, which is fed into graph convolutional networks (GCN) to learn spatial embeddings. The GCN embeddings are used along with temporal features to predict the load. We perform extensive experiments using state-of-the-art machine learning and deep learning techniques to validate our approach. The results demonstrate that by using spatial information, we can sub-stantially improve the forecasting performance.
Reliable telecommunication tower operation is paramount for sustainable cities, as it ensures uninterrupted communication, supports economic growth, facilitates smart city applications, and enables emergency response. This study evaluates the reliability and economic aspects of three hybrid system configurations aimed at providing uninterrupted power supply to base transceiver stations (BTS) during power outages. A framework is developed to optimize power operation and assess the operational costs of these configurations. A case study is conducted to examine the effectiveness of the optimization framework. The study evaluates the system size and costs of solar PV, hydrogen fuel cell, and battery energy storage system. The results demonstrate that system architecture combining a utility grid with battery energy storage and solar PV offers the most cost-effective option. The system architecture incorporating utility grid with battery energy storage and hydrogen fuel cell provides the highest reliability. Per-day operating cost of the solar PV-based architecture is 40.3% lower than that of the architecture with hydrogen fuel cell system, and 35.8% lower than the system architecture of utility grid and battery storage. This study contributes to the integration of renewable power sources and optimization framework, enhancing the sustainability of energy supply and promoting the long-term well-being of society.
Mobile Adhoc Networks (MANETs)are mainly design for node to node communication without any base station, node without transmission range support nearest node to send the data packet. The nearest node won’t send the packet to the destination node, because it act as the malicious or any valid reason. According to this situation, we need grantee to sending and dropping packet. Existing work mainly focus on the DSR based protocol, in this protocol on demand protocol and it take more time to identify the destination. In this paper use PSR protocol knowledge about the all node continuous update the routing information in routing table. The collect information using two acknowledgement based scheme in opposite of traffic route in the network. In this two acknowledgement PSR based scheme more effective compare existing acknowledgement mechaisum. This types of intrusion detection mechanism choose the alternate path in the network and more efficiency to send the data packet.
The increasing importance of electric vehicles lies in their lower emissions compared to fossil fuel vehicles.However, challenges like long charging times and range anxiety hinder their widespread adoption.Battery swapping stations offer a practical solution to expedite EV refueling, reducing wait times and range concerns.This research proposes a battery-swapping architecture that provides battery-swapping services to electric vehicles while exploring additional revenue sources and cost reductions.The model uses batteries of the battery swapping station as a battery energy storage system, supplying power to mobile or stationary loads during grid or renewable energy source downtime.By offering cost-effective electricity during peak hours or non-availability, the model demonstrates up to a 35% reduction in consumer electricity costs during peak hours and an 8.8% reduction in overall costs during 24-hour operation.The implementation combines linear programming with machine learning to forecast renewable energy output and electric vehicle energy demand, considering flexible battery charging and discharging controls and degradation processes.These optimization results show the potential of the proposed model to boost battery swapping station income and cut costs, contributing significantly to the electric vehicle market's growth.
Detection System is a security support mechanism which has received great attention from researchers all over the globe recently. In the recent past, bio-inspired meta- heuristic technique such as swarm intelligence is being proposed for intrusion detection. Swarm Intelligence approaches are used to solve complicated problems by multiple simple agents without centralized control. The swarm intelligence algorithms inspired by animal behaviour in nature such as ants finding shortest path in finding food; a flock of birds flies or a school of fish swims in unison, changing directions in an instant without colliding with each other has been successfully applied to optimization, robotics and military applications. But however, its application to the intrusion detection domain is limited but interesting and inspiring. This paper provides an overview of the research progress in swarm intelligence techniques to the problem of intrusion detection. Keywordsdetection, bio-inspired, swarm intelligence, meta-heuristic.
Demand side management (DSM) programs are an integral part of the modern grid. Most of these DSM programs are designed to work at household and hour level. The optimization problems in these DSM programs are guided by the forecasted load. An error in the hour ahead load forecasting results in a suboptimal solution entailing economic cost to both the utility and the customers. Predicting loads at a fine granularity (e.g., households) is challenging due to a large number of (known or unknown) factors affecting power consumption. At larger scales (e.g., clusters of consumers), since the inherent stochasticity and fluctuations are averaged out, the problem becomes substantially easier. Many techniques have been proposed to predict loads of clusters of consumers in various localities with great accuracy. Also, these techniques generally utilize sociological and weather information and work better on data from a particular locality. In this paper, a new technique called Past Vector Similarity (PVS) has been proposed to predict electricity load one hour ahead at the level of an individual household. The proposed strategy is based on load information only and does not require calendar, weather or any other attributes. In fact, the idea is to extract the exact load patterns of individual households corresponding to their routine and socio-economic values. Consequently, the technique makes use of the recent past vector and generate similar patterns for the prediction of future load profiles. Furthermore, the ensemble of these similar loads is an efficient prediction of electricity. PVS has just two parameters due to which it can be applied to the smaller dataset without overfitting issue. Moreover, due to the parallel nature of PVS, it can be scaled for a large number of customers without computation burden. The proposed PVS has been assessed empirically for two distinct datasets from Australia and Sweden. The simulation results demonstrate that the PVS significantly outperforms other state-of-the-art forecasting methods in terms of accuracy.
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