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Abstract Although single‐stage inverters are popular for simpler design they face difficulties in shoot‐through mode operation. Insufficient DC voltage regulation and complicated LC network are principal drawbacks of these topologies. A new split source inverter (SSI) can solve problems with single‐stage inverters. In this paper, SSI replaced the voltage source inverter (VSI) of a shunt active power filter (SAPF) which alleviates power quality (PQ) issues in power system. Furthermore, a VSI‐based SAPF faces challenges to maintain consistent voltage across DC‐link capacitor, exactly compensating current generation, and switching loss reduction in inverter. This study examined efficacy of two inverter‐based SAPFs considering a modified synchronous reference frame approach for generating reference current; fuzzy logic controller for regulating DC‐link voltage; and adaptive fuzzy hysteresis current controller for generating switching pulses. Ideal and non‐ideal sources were considered when running the simulations for various load circumstances. The proposed SSI‐based SAPF with an advanced control approach can eradicate source current harmonics to 0.52% and 0.45% under ideal source conditions, and 0.65% and 0.54% under non‐ideal source conditions for non‐linear loads. Similarly, the ripples in the DC‐link voltage are also reduced to 2–3 V for the ideal case and 4–5 V for non‐ideal case.

This paper presents an innovative approach for automatic generation control for power system under a deregulated setting. The main objective of this work is to optimally tune the parameters of the fractional-order controller using the newly developed Aquila Optimizer (AO) to enhance system performance. A test system comprising a thermal power plant, a hydroelectric system, a gas turbine-based power plant, and wind energy sources is examined under deregulated environment. The study emphasizes the minimization of frequency variations, tie line deviations, and area control errors during diverse operational shifts. The proposed control strategy explores the response of generators in a hybrid deregulated power system, emphasizing the critical role of properly tuned Fractional Order Proportional-Integral-Derivative (FOPID) controllers in ensuring system stability. The potential and effectiveness of the proposed algorithm are compared with particle swarm optimization (PSO) and whale optimization algorithm (WOA) based controller performance for the same test system. The objective function for optimization is set as the minimization of the integral time and absolute error (ITAE) performance index. Furthermore, the efficacy of the proposed technique is compared with the Unified Power Flow Controller (UPFC) and its superiority is validated. Performance evaluation of the hybrid power system is conducted under Poolco agreement, bilateral agreement, and varying operating conditions. Comparative assessments reveal the superiority of the AO-driven FOPID over other techniques, demonstrating improved system metrics, including frequencies across different areas, tie-line power variations, and generator outputs.

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.

Drinking water is the most precious resource on Earth. In the past few decades, the quality of drinking water has significantly degraded due to pollution. Water quality assessment is paramount for the well-being of the people since the presence of pollutants can have serious health issues. Particularly, in developing countries such as India, water is not properly assessed for its quality. This work uses machine learning techniques to predict the water quality of Indian rivers. It focuses on finding water potability when provided with the key factors used to calculate the water quality index for the water sample. Important parameters like water temperature, pH value, electrical conductivity, dissolved oxygen, fecal coliform, total coliform counts, and biochemical oxygen demand are used to calculate the water quality index. The approaches that are explored include the use of K-nearest neighbor (KNN), Random Forest, and XGBoost, with and without hyperparameter tuning, and the use of a sequential artificial neural network to see which of the three models helps us to give the most accurate predictions for the potability of water. XGBoost was the most efficient model, with an accuracy of 98.93%. HIGHLIGHTS The extreme gradient boosting classifier (XGBoost) model has been used for assessing the water potability of Indian rivers.; Comparison is shown with some existing works and the results are much better.; Case studies are applied on Indian rivers.; Four different models are used: K-nearest neighbor, XGBoost, Random Forest, and artificial neural networks.;

AbstractDevOps is a potential solution to time and quality restrictions in software development. It aims to increase the frequency, quality, and speed of software delivery using automated procedures. DevOps is a culture transformation, not just a toolchain. DevOps emphasizes cooperation, automation, measurement, information sharing, and web service utilization. It positively impacts IT development, online services, and quality assurance. Before commencing the DevOps journey, it is necessary to understand DevOps principles, practices, tools, benefits, and underlying issues. Such vital parameters are critically reviewed in this article. This systematic review addresses gaps and recommendations related to DevOps, aiming to provide a comprehensive understanding of its culture and mindsets. The article presents an in-depth examination of DevOps, covering topics like architecture, components, tools, principles, and security challenges. It establishes a conceptual framework for practical implementation. Security has also been discussed in the paper, which is one of the difficult problems in DevOps implementations. The research findings aid in a better understanding of the phenomenon from a human factors perspective. The state-of-the-art discussion on several tools covering architectural and networking aspects in DevOps is included in this article to attract practitioners and researchers for DevOps adoption. Our analysis revealed three key themes related to DevOps culture and mindset: collaboration, continuous improvement, and automation. Moreover, DevOps is not immune to challenges. The proposed work presents the existing gaps and future research directions to address the same.

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.

Renewable energy-based hybrid power systems are being increasingly deployed across the globe to reduce carbon emissions. The system frequency and tie-line power tend to fluctuate due to system operating conditions. These fluctuations can be maintained within the desired limits using optimally designed controllers. In this article, the recently developed improved squirrel search algorithm (ISSA) is used to tune the parameters of different controllers, such as the proportional-integral-differential (PID), two degrees of freedom PID (2DOF-PID), three degrees of freedom PID (3DOF-PID), and a cascaded 2DOF-PID fractional order integral (FOI) to improve the performance of the system. The effectiveness of the best controller tuned with ISSA is compared with other optimization techniques such as Particle Swarm Optimization (PSO) and Squirrel Search Algorithm (SSA). A two-area multi-machine hybrid power system is considered to demonstrate the robustness of the proposed concept. The first area consists of a thermal, a hydro, and a wind power plant, while the second area consists of a thermal, a hydro, and a diesel power plant. Comparative performance analysis is carried out to obtain the best controller, tuned with the best optimization technique. Various case studies have been developed to check the system’s robustness, flexibility, and reliability.

This paper presents ocean wave energy control using the Aquila optimization (AO) technique. An oscillating water column (OWC)-type wave energy converter has been considered that is fitted with a Wells turbine and doubly fed induction generator (DFIG). To achieve maximum power point tracking (MPPT), the rotor speed of the DFIG must be controlled as per the MPPT law. The MPPT law is designed in such a way that the Wells turbine flow coefficient remains within the threshold limit. It avoids the turbine from stalling which generates the maximum power. The MPPT law provides the reference rotor speed which is followed by the actual rotor speed. For this, a backstepping controller (BSC)-based rotational speed control strategy has been designed using the Lyapunov stability theory. The BSC has unknown control parameters which should be selected such that tracking errors are minimum. Hence, the objective of this work is to find the unknown control parameters using an optimization approach. The optimization approach of selecting BSC control parameters for an OWC plant has not been explored yet. To achieve this, an integral square error (ISE)-type fitness function has been defined and minimized using the AO technique. The results achieved using the AO technique have been compared with particle swarm optimization (PSO) and a genetic algorithm (GA), validating its superior performance. The rotor speed error maximum peak overshoot is least for AO-BSC as compared to PSO-BSC and GA-BSC. The fitness function value for AO comes out to be least among all the optimization methods applied. However, all tested methods provide satisfactory results in terms of turbine flow coefficient, rotor speed and output power. The approach paves the way for future research on ocean wave energy control.

The power grid is evolving into a smart grid due to the diverse energy generation and distribution. This complex grid has to be continuously monitored in real-time for its safe operation. Sensors known as phasor measurement units (PMUs) are used for obtaining health information pertaining to the grid in terms of time-synchronized voltage and current phasors. Measurements from several PMUs are sent through a synchrophasor communication network (SCN) to the phasor data concentrator (PDC). The PMUs, the PDC and the SCN together constitute the wide area measurement system (WAMS). Being an important constituent of the WAMS, the resiliency estimation of SCNs is paramount for their proper design. Resilience is a measure of the systems resistance to the disturbances or a measure of its ability to bounce back to a functional state in the event of failure. This paper presents a quantitative metric for estimating the resiliency of SCNs. Monte Carlo simulation (MCS) models are used to simulate random component failures, and the data is used for measuring the resiliency of the SCNs. A multi-objective genetic algorithm (GA) is used for optimizing the placement of PMUs and the PDC, to observe the power system with the minimum number of PMUs, and to simultaneously maximize the resilience. The practical power grid of West Bengal, India, is analyzed as a case study. This work can be a significant contribution to the power sector as it assists in the proper planning and placement of the communication infrastructure in a WAMS.

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance.

In recent years, the Smart Grid (SG) has been conceptualized as a burgeoning technology for improvising power systems. The core of the communication infrastructure in SGs is the Synchrophasor Communication Network (SCN). Using the SCN, synchrophasor data communication is facilitated between the Phasor Measurement Unit (PMU) and Phasor Data Concentrator (PDC). However, the SCN is subjected to many challenges. As a result, the components, such as the links, PMUs, PDCs, nodes, etc., of the SCN are subjected to failure. Such failure affects the operation of the SCN and results in the performance degradation of the SG. The performance degradation of the smart grid is observed either temporarily or permanently due to packet loss. To avoid dire consequences, such as a power blackout, the SCN must be resilient to such failures. This paper presents a novel analytical method for the resiliency analysis of SCNs. A graph-theoretic approach was used to model SCN from the resiliency analysis perspective. Furthermore, we proposed a simulation framework for validating the analytical method using the Network Simulator-3 (ns-3) software. The proposed non-intrusive simulation framework can also be extended to design and analyse the resiliency of generic communication networks.

Metamaterial absorbers are used in the terahertz frequency regime as photo-detectors, as sensing elements, in imaging applications, etc. Narrowband absorbers, on account of their ultra-slender bandwidth within the terahertz frequency spectrum, show a significant shift in the absorption peak when an extrinsic entity relative to the absorber, like refractive index or temperature of the encircling medium, is altered. This property paves the path for the narrowband absorbers to be used as potential sensors to detect any alterations in the encircling medium. In this paper, a novel design of a terahertz metamaterial (MTM) absorber is proposed, which can sense the variations in the refractive index (RI) of the surrounding medium. The effective permeability of the structure is negative, while its permittivity is positive; thus, it is a μ-negative metamaterial. The layout involves a swastika-shaped design made of gold on top of a dielectric gallium arsenide (GaAs) substrate. The proposed absorber achieved a nearly perfect absorption of 99.65% at 2.905 terahertz (THz), resulting in a quality factor (Q-factor) of 145.25. The proposed design has a sensitivity of 2.12 THz/RIU over a range of varied refractive index from n = 1.00 to n = 1.05 with a step size of 0.005, thereby achieving a Figure of Merit (FoM) of 106. Furthermore, the sensor was found to have a polarization-insensitive characteristic. Considering its high sensitivity (S), the proposed sensor was further tested for gas sensing applications of harmful gases. As a case study, the sensor was used to detect chloroform. The proposed work can be the foundation for developing highly sensitive gas sensors.

The smart grid (SG), which has revolutionized the power grid, is being further improved by using the burgeoning cyber physical system (CPS) technology. The conceptualization of SG using CPS, which is referred to as the smart grid cyber physical system (SGCPS), has gained a momentum with the synchrophasor measurements. The edifice of the synchrophasor system is its communication network referred to as a synchrophasor communication network (SCN), which is used to communicate the synchrophasor data from the sensors known as phasor measurement units (PMUs) to the control center known as the phasor data concentrator (PDC). However, the SCN is vulnerable to hardware and software failures that introduce risk. Thus, an appropriate risk assessment framework for the SCN is needed to alleviate the risk in the protection and control of the SGCPS. In this direction, a comprehensive risk assessment framework has been proposed in this article for three types of SCNs, namely: dedicated SCN, shared SCN and hybrid SCN in an SGCPS. The proposed framework uses hardware reliability as well as data reliability to evaluate the associated risk. A simplified hardware reliability model has been proposed for each of these networks, based on failure probability to assess risk associated with hardware failures. Furthermore, the packet delivery ratio (PDR) metric is considered for measuring risk associated with data reliability. To mimic practical shared and hybrid SCNs, the risk associated with data reliability is evaluated for different background traffics of 70%, 80% and 95% using 64 Kbps and 300 Kbps PMU data rates. The analytical results are meticulously validated by considering a case study of West Bengal’s (a state in India) power grid. With respect to the case study, different SCNs are designed and simulated using the QualNet network simulator. The simulations are performed for dedicated SCN, shared SCN and hybrid SCN with 64 Kbps and 300 Kbps PMU data rates. The simulation results are comprehensively analyzed for risk hedging of the proposed SCNs with data reliability and hardware reliability. To summarize, the mean risk with data reliability (RwDR) as compared to the mean risk with hardware reliability (RwHR) increases in shared SCN and hybrid SCN by a factor of 17.108 and 23.278, respectively. However, minimum RwDR increases in shared and hybrid SCN by a factor of 16.005 and 17.717, respectively, as compared to the corresponding minimum RwHR. The overall analysis reveals that the RwDR is minimum for dedicated SCN, moderate for shared SCN, and highest for hybrid SCN.

This paper presents an optimized algorithm for event-triggered control (ETC) of networked control systems (NCS). Initially, the traditional backstepping controller is designed for a generalized nonlinear plant in strict-feedback form that is subsequently extended to the ETC. In the NCS, the controller and the plant communicate with each other using a communication network. In order to minimize the bandwidth required, the number of samples to be sent over the communication channel should be reduced. This can be achieved using the non-uniform sampling of data. However, the implementation of non-uniform sampling without a proper event triggering rule might lead the closed-loop system towards instability. Therefore, an optimized event triggering algorithm has been designed such that the system states are always forced to remain in stable trajectory. Additionally, the effect of ETC on the stability of backstepping control has been analyzed using the Lyapunov stability theory. Two case studies on an inverted pendulum system and single-link robot system have been carried out to demonstrate the effectiveness of the proposed ETC in terms of system states, control effort and inter-event execution time.

The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.