Nowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy reso...
详细信息
Nowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy resources. The sensor nodes are highly affected by collision due to the transmission of packets over the network by one or two nodes at the same time. Collision detection is necessary to increase network security and enhance the lifetime of sensor nodes. In most of the previous research, efficiently implementing collision detection algorithms while minimizing resource usage remains a significant challenge. Thus, a hybrid deep learning model deep Kronecker recurrent neural network (DKRNN) is developed in this research. Here, the cluster head is selected using the chronological skill optimization algorithm (CSOA) algorithmic approach by considering multi-objective parameters like energy, distance, delay, and trust. The network-based parameters are then extracted from the network. Later, the collision is detected using the DKRNN approach and the collision is mitigated finally using a packet pre-scheduling model named Dolphin Ant Lion optimization (Dolphin ALO). Moreover, the detection performance of CSOA+ DKRNN is validated, and it achieved superior performance with a collision detection rate (CDR) of 0.940, packet delivery ratio (PDR) of 0.660, throughput of 0.850Mbps, and energy consumption of 0.110 J. In this research, the cluster head selection is performed by using the CSOA, which is the integration of the Chronological concept and SOA. Moreover, the collision detection is done by using the DKRNN model, which is the hybridization of DRNN and DKN. Finally, the collision mitigation is carried out by using Dolphin ALO, which is a combination of ALO and DE. image
This research introduces a novel deep learning for channel estimation for the 6G networks. The channel estimation for the 6G networks is employed using the proposed Improved Convolutional Recurrent (ImConv-RNN) model....
详细信息
Continuous growth in size and complexity, stochastically changing power demands, system modeling errors, alterations in electric power system structures and variations in the system parameters over the time have turne...
详细信息
Continuous growth in size and complexity, stochastically changing power demands, system modeling errors, alterations in electric power system structures and variations in the system parameters over the time have turned automatic generation control (AGC) task into a challenging one. Hence, in this article, design of a neuro-fuzzy-based three-degree-of-freedom-PD with a low pass filter coefficient (NF-3DOF-PDN) controller for a three-area AGC system is examined in this study. Areas 1, 2, and 3 are considered hydrothermal power plants. Several secondary controllers, such as 2DOF-PDN, 3DOF-PDN, and NF-3DOF-PDN controllers, have been examined separately to maintain the frequency and tie line power. Physical restrictions, such as the generation rate constraints and the time delay, have been incorporated into the system for a more realistic approach. A skill optimization algorithm (SOA) is used to optimize the controller gains and other parameters with integral squared error as performance indices. Numerous simulations are investigated to demonstrate the superiority of the proposed NF-3DOF-PDN controller over existing secondary controllers. The impacts of combining hydrogen aqua electrolyze (HAE) and fuel cell (FC) units on dynamic systems are being studied in this system. Moreover, a unified power flow controller (UPFC) is also incorporated with the tie lines to strengthen the systems against low-frequency damping oscillations. The resilience of the SOA-optimized proposed NF-3DOF-PDN controller has also been investigated for various system loading conditions. The performance of the proposed NF-3DOF-PDN with HAE-FC and UPFC controller under nominal conditions is resilient, and it is not required for several time resets of the controller while the system loading varies.
This article deals with the AGC design of diverse sources-integrated multi-area systems. Area 1 is the combination of solar PV, wind farm, and thermal units. Areas 2 and 3 combine wind farms, hydropower, and thermal u...
详细信息
This article deals with the AGC design of diverse sources-integrated multi-area systems. Area 1 is the combination of solar PV, wind farm, and thermal units. Areas 2 and 3 combine wind farms, hydropower, and thermal units. skill optimization algorithm (SOA) is used to simultaneously optimize secondary controllers' gains and other parameters. The various helpful performance indices, like ITSE, ISE, ITAE, and IAE, are compared, and it found that the ISE is the best performance indices. The proposed cascaded NF-PDF-PIDF controller outperforms PDF-PIDF and PIDF in terms of the system's dynamic performance when compared to those two. It is also found that integrating solar PV and energy storage, like SMES, proves dynamic responses. The impacts of combining HVDC and AC tie-lines on system response are also studied. It is found that parallel AC-HVDC tie-lines provide better dynamics than AC tie-lines alone. Finally, the resiliency of the SOA-optimized proposed cascaded (CNF-PDF-PIDF) has been applied for a broad change of system loading. It is revealed that the CNF-PDF-PIDF controller's performances at nominal conditions are resilient, and there is no need to reset the controller multiple times when the system loading varies.
暂无评论