The high impedance faults (HIF) in microgrids are characterized by low magnitude fault current with inherent non‐linearity and random behavior. The possible weather intermittency in PV integrated ...microgrids and the intrusion of harmonics due to power converters and non‐linear loading, further reduce the fault current during HIF. The insignificant fault current does not allow detection of HIF by conventional overcurrent relays. In this regard, a reliable and robust protection scheme based on the combined framework of probabilistic modelling, least square and Adaline algorithm, sine cosine algorithm (SCA) and support vector machine (SVM), has been proposed to perform the tasks of mode detection, fault detection/classification and section identification during HIF in microgrid. The algorithm involves online extraction of harmonic components and stochastic modelling of intermittency in solar irradiance. The performance of the proposed scheme has been analysed in terms of robustness against harmonics intrusion and weather intermittency during HIF. The performance comparison of the proposed scheme with the decision tree (DT) and classical SVM based techniques reflects its effectiveness with improved immunity against harmonic intrusion and weather intermittency. Further, the proposed scheme has been validated using OPAL‐RT digital simulator for evaluating its appropriateness in real‐time settings.
The similar current–voltage profile during power swing and fault quite often leads to maloperation of distance relays. As compared to symmetrical power swings, discriminating a swing scenario from a ...fault becomes more challenging during asymmetrical swings arising due to single-pole tripping. Unlike symmetrical power swings, the presence of zero-sequence and negative-sequence current during asymmetrical swing scenarios hinders the application of classical power swing blocking schemes. In this regard, a convolutional neural network (CNN)-based protection scheme has been proposed in this paper, which, in addition to detecting, classifying, and locating faults, is also able to discriminate between power swing (both symmetrical and asymmetrical) and faults. The discrimination avoids possible maloperation during the non-faulty stressed conditions, thereby overcoming the limitation of the existing protection scheme. With the convolutional neural network, the raw signals are directly fed to the classifier, thus avoiding the computational cost associated with feature extraction in time and frequency domains. With the aim of achieving improved input–output mapping capability of CNN for larger datasets, an evolutionary optimization technique, i.e., grey wolf optimization, has been utilized for determining the optimal values of CNN tuning parameters. The performance of the proposed scheme has been extensively validated for a wide range of fault and power swing conditions in terms of standard indices, i.e., dependability, security, and accuracy. The effectiveness of the proposed scheme has also been evaluated for practical setting by performing real-time simulation on OPAL-RT digital simulator.
To meet the rising power demand, it has become mandatory to consider alternative solutions to the existing transmission line framework. Six phase transmission line can be a potential solution due to ...its ability to transfer 73% more power relative to classical three phase double circuit system. However, due to the increased number of series and shunt faults in the six-phase transmission line, the development of an accurate protection scheme is quite challenging in a six-phase system. Also, because of the larger number of phases, the possibility of non-typical shunt faults, viz. evolving and cross-country faults, are quite high in a six-phase system. Hence, the significance of developing a protection scheme with increased sensitivity against all types of faults (series, shunts, evolving, and cross-country) has become vital for the acceptance of six-phase transmission lines by power utilities. In this regard, a random forest (RF)-based classifier and regression approach have been respectively proposed for the detection/classification and location estimation of all possible faults in a six-phase transmission line. Separate RF classifier modules have been developed for shunt and series faults for detection and classification, followed by further categorization of shunt faults into evolving and cross-country faults. For location estimation, the high dimensional nonlinear mapping characteristics of the Regression Tree (RT) have been utilized. The use of single-end data, avoids the issue of latency and data loss associated with communication assisted protection schemes. The proposed RF-based scheme's efficacy has been tested for various types of typical and non-typical shunt and series faults, with a wide range of fault and power system parameters. The test findings of the RF-based protection illustrate that the protection scheme is resistant to a wide variation in the fault types, fault conditions, extent of nonlinear loading, and noise intrusion.
Wide area monitoring and control of modern power network demand real-time estimation of state variables from sensor measurements. Maintaining a high degree of reliability and accuracy in the state ...estimation process is important in avoiding any disruption in the electricity market operation. The market operation in power networks aims at providing a win-win situation for both the utility and consumer. The exposure and vulnerability of cyber components in smart grids allow for manipulating the electricity market by falsifying the state variables. The attacker can cause intentional profit/loss to the utility/consumer by misdirecting the estimated states through the injection of false data into the sensor information. Hence, maintaining integrity in the market operation demands a mechanism for detecting false data injection attack (FDIA). This paper proposes a classification-based approach for detecting FDIAs aiming at electricity market disruption. For any variation in the predicted and real-time nodal electricity price, the proposed decision tree (DT) based ensemble classifier is executed using state information to identify the prevailing scenario as a contingency or FDIA. The effectiveness of the proposed scheme has been extensively validated for various contingency and FDIA scenarios in IEEE 14 bus, 39 bus, and 57 bus test power systems.
The brain-computer interface (BCI) identifies brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper, identification and ...monitoring of electroencephalogram-based BCI for motor imagery (MI) task is proposed by an efficient adaptive neuro-fuzzy classifier (NFC). The Jaya optimization algorithm is integrated with adaptive neuro-fuzzy inference systems to enhance classification accuracy. The linguistic hedge (LH) is used for proper elicitation and pruning of the fuzzy rules and network is trained using scaled conjugate gradient (SCG) and speeding up SCG (SSCG) techniques. In this paper, Jaya-based k-means is applied to divide the feature set into two mutually exclusive clusters and fire the fuzzy rule. The performance of the proposed classifier, Jaya-based NFC using SSCG as training algorithm and is powered by LH (JayaNFCSSCGLH), is compared with four different NFCs for classifying two class MI-based tasks. We observed a shortening of computation time per iteration by 57.78% in the case of SSCG as compared with the SCG technique of training. LH-based feature selecting capability of the proposed classifier not only reduces computation time but also improves the accuracy by discarding irrelevant features. Lesser computation time with fast convergence and high accuracy among considered NFCs make it a suitable choice for the real-time application. Supremacy of JayaNFCSSCGLH among the considered classifier is validated through Friedman test. Classification result is used to control switching of light emitting diode, turning thoughts into action.
We present a green synthesis method of producing blue fluorescence emitting carbon nanoparticles (CNPs) through a simple and cost-effective single-step hydrothermal reaction. The synthesis utilized ...bract extracts and pollen grains from three
Bougainvillea
species:
B. spectabilis, B. alba,
and
B. buttiana
. The CNPs exhibited photoluminescence, with the highest emission observed in the ultraviolet region. Atomic force microscopy analysis revealed that the size of synthesized CNPs ranged from 23 to 83 nm. Fourier transform infrared analysis provided a comprehensive understanding of the CNP’s surface functional groups, with carbon being the predominant group. X-ray diffraction analysis confirmed the amorphous nature of the synthesized CNPs. Zeta potential measurements indicated that the particles carried a negative charge, suggesting their colloidal stability. In experiments conducted with
Arabidopsis thaliana
seedlings, CNPs derived from
B. alba
pollen grains (Ba-POL) were found to promote leaf area expansion while simultaneously inhibiting primary root growth. In presence of 10 nM IAA, Ba-POL CNP treated seedlings displayed a significant reduction in primary root growth. RT-PCR analysis revealed substantial changes in the expression of auxin response genes in presence of Ba-POL CNP. Cytokinin levels were enhanced in the seedlings in the presence of Ba-POL CNP, as revealed increased expression of cytokinin reporter
TCS::GFP
. Our findings underscore the potential application of these novel CNPs in modulation of plant growth through its effects on auxin–cytokinin signaling.
Quadrilateral characteristics of distance relays are widely used in modern power systems because of its lesser sensitivity to the variation in operating scenarios and fault parameters. The ...performance of quadrilateral relay is heavily dependent on the proper selection of reach settings. The reach accuracy of distance relays with settings estimated using conventional techniques deteriorates during stressed conditions such as load variation, power swings and remote end infeed/outfeed variations. A proper setting in addition to avoiding overreach and underreach cases should also posses higher loadability limit. In this regard, the relay reach setting has been mathematically formulated as a constrained optimization problem in the present work, with the aim of maintaining the trade-off between the reach accuracy and the loadability limit in terms of area enclosed by the relay characteristics. Sine–cosine algorithm has been adopted to solve the multiobjective optimization problem and hence obtain the reach setting of the quadrilateral relay. The performance of the proposed scheme in terms of providing optimal reach setting has been extensively examined against wide variation in fault parameters and operating scenarios. Further, in order to test the effectiveness of the proposed scheme for real-time applications, the suitability of the quadrilateral relay has also been validated by performing real-time simulations using OPAL-RT digital simulator.
In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying ...the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence.
An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation.
A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed. The initial population for the hybrid technique is obtained from the two conventional local- based approaches, i.e., Fuzzy C-means and K-means clustering algorithms.
The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with a Jaccard Index of 0.91 from 720 psoriasis images.
The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.
P300 speller-based brain-computer interface is a direct communication from human-brain to computer machine without any muscular movements. In conventional P300 speller, a display paradigm is used to ...present alphanumeric characters to users and a classification system is used to detect the target character from the acquired electroencephalographic signals. In this paper, we present an 8 × 8 matrix consisting of Devanagari characters, digits, and special characters as Devanagari script (DS)-based display paradigm. The larger size of the display paradigm as compared with conventional 6 × 6 English row/column (RC) paradigm, involvement of matras and ardha-aksharas and similar looking characters in DS increase the adjacency problem, crowding effect, fatigue, and task difficulty. This results in deteriorated performance at the classification stage. Binary differential evolution algorithm was employed for optimal channel selection and support vector machine was used to classify target verses non target stimuli for the data set collected from ten healthy subjects using the DS-based paradigm. In order to further improve the system reliability in terms of higher accuracy at word prediction level, this paper proposes a novel spelling correction approach based on weighted edit distance (WED). A custom-built dictionary was incorporated and each misspelled word was replaced by a correct word of minimum WED from it. The proposed work is based on the validation of hypothesis that most of the target-error pairs lie in the same RC. Using the proposed spelling correction approach with optimal channel selection, an average accuracy of 99% was achieved at the word prediction level. The statistical analysis carried out in this paper shows that the proposed WED-based method improves the system reliability by significantly increasing in the accuracy of word prediction. This paper also validates that the proposed method performs better as compared to the conventional edit distance-based spelling correction approach.
Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism ...in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K -means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K -means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K -means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.
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