Centralized Airflow Control to Reduce Output Power Variation in a Complex OWC Ocean Energy Network

A centralized airflow control scheme for a complex ocean energy network (OEN) is proposed in this paper to reduce the output power variation (OPV). The OEN is an integrated network of multiple oscillating water columns (OWCs) that are located at different geographical sites connected to a common electrical grid. The complexity of the OWC-OEN increases manifolds due to the integration of several OWCs and design of controllers become very challenging task. So, the centralized airflow control scheme is designed in two stages. In control stage-1, a proportional-integral- (PI-) type controller is designed to provide a common reference command to control stage-2. In control stage-2, the antiwindup PID controllers are implemented for the airflow control of all the OWCs simultaneously. In order to tune the large number of control parameters of this complex system, a fitness function based on integral squared error (ISE) is minimized using the widely adopted particle swarm optimization (PSO) technique. Next, the simulation results were obtained with random wave profiles created using the Joint North Sea Wave Project (JONSWAP) irregular wave model. The OPV of the proposed OWC-OEN was reduced significantly as compared to the individual OWC. It was further observed that the OPV of the proposed scheme was lower than that achieved with uncontrolled and MPPT controlled OWC-OEN. The effect of communication delay on the OPV of the proposed OWC-OEN scheme was also investigated with the proposed controller, which was found to be robust for a delay up to 100 ms.This work was supported in part by the Basque Government through project IT1207-19 and MCIU/MINECO through RTI2018-094902-B-C21/RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE)

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%

A very compact metamaterial-based triple-band sensor in terahertz spectrum as a perfect absorber for human blood cancer diagnostics

In this dynamic environment, early cancer identification and surveillance have become vital problems. This research paper explores the development of a small, three-band sensor harnessing the potential of terahertz (THz) technology and metamaterials (MTMs) to diagnose blood cancer. This sensor holds the promise of a paradigm shift in the diagnosis and treatment of blood cancer by offering a non-invasive, highly accurate approach. Terahertz radiation, occupying the unique "THz gap" in the electromagnetic spectrum, is now accessible due to recent technological breakthroughs. This work simplifies the design of multiple-band metamaterial absorbers, enhancing their effectiveness and expanding their sensing capabilities. Through the integration of THz technology, metamaterial engineering, and cancer detection, the suggested sensor seeks to launch a new phase of rapid, precise, and non-invasive blood cancer diagnosis. The proposed structure capable of distinguishing cancer and normal cell with 1GHz sensitivity. Although, this difference looks similar it would be easy when we consider the THz technology devices. This work represents a significant step forward in non-invasive, accurate diagnostics for blood cancer, promising to revolutionize the way this disease is diagnosed and treated. The novel strategy put out has a lot of promise to advance medical diagnostics and enhance patient outcomes

A review on synchrophasor communication system: communication technologies, standards and applications

Abstract The present-day power system is a complex network that caters to the demands of several applications with diverse energy requirements. Such a complex network is susceptible to faults caused due to several reasons such as the failure of the equipment, hostile weather conditions, etc. These faults if not detected in the real-time may lead to cascading failures resulting in a blackout. These blackouts have catastrophic consequences which result in a huge loss of resources. For example, a blackout in 2004 caused an economic loss of 10 billion U.S dollars as per the report of the Electricity Consumers Resource Council. Subsequent investigation of the blackout revealed that the catastrophe could have been prevented if there was an early warning system. Similar other blackouts across the globe forced the power system engineers to devise an effective solution for real-time monitoring and control of the power system. The consequence of these efforts is the wide area measurement system (WAMS). The WAMS consists of several sensors known as the phasor measurement units (PMUs) that collect the real information pertaining to the health of the power grid. This information in the form time synchronized voltage and current phasors is communicated to the central control center or the phasor data concentrator (PDC) where the data is analyzed for detection of power system anomalies. The communication of the synchrophasor data from each PMU to the PDC constitutes the synchrophasor communication system (SPCS). Thus, the SPCS can be considered as the edifice of the WAMS and its reliable operation is essential for the effective monitoring and control of the power system. This paper presents a comprehensive review of the various synchrophasor communication technologies, communication standards and applications. It also identifies the existing knowledge gaps and the scope for future research work

Resiliency assessment methodology for synchrophasor communication networks in a smart grid cyber–physical system

The smart grid cyber–physical system (SGCPS) is the latest evolution of the traditional power system. Synchrophasor application in the SGCPS is responsible for wide area monitoring and control of the grid. Its communication network referred to as the synchrophasor communication network (SCN) has to be resilient. The resiliency measures the system’s ability to bounce back to the operational state from the failed state. Despite the maturity of the research on resiliency, it is still sparsely explored for the SCNs in a SGCPS. There is no comprehensive resiliency metric for the resiliency analysis of the SCN. Thus, a comprehensive resiliency metric in the context of the SCNs is presented in this paper. Further, a methodology is also presented for evaluating the resiliency of the SCN. The SCNs are designed for the practical power grid of West Bengal State, India, which have been analyzed for their resiliency using the proposed methodology

OWC-based ocean wave energy plants

This book discusses about the new techniques of power generation control of oscillating water column (OWC) using airflow control and maximum power point tracking of OWC using rotational speed control. OWCs harness energy from the oscillation of the seawater inside a chamber or hollow caused by the action of waves. This book presents the mathematical modeling and control techniques used by OWCs. Introducing new concepts to studies of wave energy to provide fresh perspectives on energy extraction and efficiency problems, the book will be a valuable resource for researchers and industrial companies involved in thermal energy and coastal engineering. It will also be of interest to students, as it broadens their view of wave energy

Challenges and Solutions for Vehicular Ad-Hoc Networks Based on Lightweight Blockchains

Current research with Vehicular Ad-hoc Networks (VANETs) has focused on adapting an efficient consensus mechanism and reducing the blockchain size while maintaining security. Care must be taken when implementing blockchains within VANET applications to leverage the chains’ strengths while mitigating their weaknesses. These chains can serve as distributed ledgers that provide storage for more than financial transactions. The security provided by longer blockchains constitutes a nearly immutable, decentralized data structure that can store any data relevant to the applications. However, these chains must be adapted to the ad-hoc, resource-constrained environments found in VANETs. In the absence of abundant resources and reliable network connections, chain operation and maintenance must address the challenges presented by highly mobile nodes in novel ways, including situations such as emergency messaging that require real-time responses. Researchers have included different mechanisms to realize lightweight blockchains, such as adding reputation to existing consensus mechanisms, condensing the consensus committees, using geographical information, and monitoring a nodes behavior in attempts to adapt blockchains to these domains. This paper analyzes the challenges and gives solutions for these different mechanisms to realize lightweight blockchains for VANETs

A Flower Pollination Algorithm-Optimized Wavelet Transform and Deep CNN for Analyzing Binaural Beats and Anxiety

Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only looked at the impact of binaural beats on state and trait anxiety using the STA-I scale; the level of anxiety has not yet been evaluated, and for the removal of artifacts the improper selection of wavelet parameters reduced the original signal energy. Hence, in this research, the level of anxiety when hearing binaural beats has been analyzed using a novel optimized wavelet transform in which optimized wavelet parameters are extracted from the EEG signal using the flower pollination algorithm, whereby artifacts are removed effectively from the EEG signal. Thus, EEG signals have five types of brainwaves in the existing models, which have not been analyzed optimally for brainwaves other than delta waves nor has the level of anxiety yet been analyzed using binaural beats. To overcome this, deep convolutional neural network (CNN)-based signal processing has been proposed. In this, deep features are extracted from optimized EEG signal parameters, which are precisely selected and adjusted to their most efficient values using the flower pollination algorithm, ensuring minimal signal energy reduction and artifact removal to maintain the integrity of the original EEG signal during analysis. These features provide the accurate classification of various levels of anxiety, which provides more accurate results for the effects of binaural beats on anxiety from brainwaves. Finally, the proposed model is implemented in the Python platform, and the obtained results demonstrate its efficacy. The proposed optimized wavelet transform using deep CNN-based signal processing outperforms existing techniques such as KNN, SVM, LDA, and Narrow-ANN, with a high accuracy of 0.99%, precision of 0.99%, recall of 0.99%, F1-score of 0.99%, specificity of 0.999%, and error rate of 0.01%. Thus, the optimized wavelet transform with a deep CNN can perform an effective decomposition of EEG data and extract deep features related to anxiety to analyze the effect of binaural beats on anxiety levels

A Reconfigurable Terahertz Metamaterial Absorber for Gas Sensing Applications

Reconfigurable metamaterials have immense applications in sensing. A refractive index reconfigurable terahertz metamaterial absorber was investigated in this research for gas sensing applications. The absorption spectrum reconfigures with the changes in the surrounding medium’s refractive index. The proposed absorber displays positive permittivity and negative permeability at the resonance frequency of 3.045 THz indicating magnetic resonance. The design consists of concentric U-shaped rings that were optimally designed to perform the parametric analysis using the finite element method (FEM). The absorption bands offered by the structure were found to be insensitive to variation in polarization angles up to 60°. The outcome of this design approach yields a 99.75% absorption rate with a Q-factor of 87. Additionally, the equivalent circuit model of this proposed absorber was analyzed to estimate the resonance frequency, which reveals good agreement with the simulated ones. Moreover, the structure was designed for a refractive index ranging between 1 and 1.03 to detect harmful gases such as methane, chloroform, etc., with a high sensitivity of 3.01 THz/RIU (Refractive Index Unit) and figure of merit (FoM) of 86. This research work is potentially suitable for biological sensing and chemical industry applications