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Accurate PV power forecasting techniques are a prerequisite for the optimal management of the grid and its stability. This paper presents a review of the recent developments in the field of PV powe...

India is a major producer and consumer of seed spices. There are numbers of studies have been done on soil and crop parameters of these but there is lacuna on the study of soil bacterial community analysis in the seed spices. Soil microorganisms are known to play critical role in soil biochemical processes, nutrient cycling, soil fertility, and also influences plant performances. With the objectives to know microbial community composition and soil chemical and biological properties in rhizosphere of two seed spices plants- cumin and coriander we collected five soil samples from rhizosphere of cumin (NRCSS-SL-1 and NRCSS-SL-3) and coriander (NRCSS-SL-2, NRCSS-SL-4 and NRCSS-SL-5) grown in arid and semi-arid regions of India. Electrical conductivity (0.32–0.53 ds/m), organic carbon (0.03–0.64%), total nitrogen (223–258 kg/ha), total phosphorous (30–47.3 kg/ha) and total potash (206–388 kg/ha) were varied in samples. In biological properties bacterial and fungal populations were varied between 98.33 and 210 c.f.u. × 104 g−1 soil and 32–69.33 c.f.u. × 103 g−1 soil, respectively. Different soil enzyme activities dehydrogenase (37.96–57.68 µg TPF g−1 soil 24 h−1), acid phosphatase (14.66–24.72 µg p-NP g−1 soil h−1), and alkaline phosphatase (27.08–37.58 µg p-NP g−1 soil h−1) were varied in samples. Soil chemical and biological activities were found highest in NRCSS-SL-2 followed by NRCSS-SL-5 and lowest in NRCSS-SL-1. Amplicon sequencing of 16S rRNA gene was done in the collected samples to identify and quantify the bacterial community structure. A total 37 phyla were observed with varying abundances in different samples. Next generation sequencing shows that three phyla were present in order of Firmicutes (26–43%) > Proteobacteria (23–29%) > Actinobacteria (11–22%) and they were dominating in all the soil samples. Relative abundance of order Bacillales was highest (26–35%), followed by Rhizobiales (8–10%) and Actinomycetales (7–11%) in samples. There were many orders which found in very low abundance and moreover some were not present in all soil samples but these may play significant role in the soil ecosystem. The study concludes that rhizosphere soil of seed spices harbors wide range of unculturable microbes and climatic conditions have impact on their community structure. Microbial activities in terms of microbial colony count and enzyme activity are also influenced by soil chemical properties.

PurposeThis proposed drone is used for surveillance purpose like medical, agriculture and military in the commercial point of view with less cost and size.Design/methodology/approachDuring emergency calls out the technology enabled modes to have quick and timely response for the mankind. As human society continues to spend months together locked inside their homes, it leads to the entire change in the human lifestyle. This also demands the society and the government to get adopt with the technological concepts such as drones to handle this pandemic scenario in a more scientific and safe mode. The major constraints in the utility segment is the cost and performance factor of the drones. This paper aims to design a drone flight management system, which can be used to operate single or multiple drone systems in a wireless mode. The major focus of this work is to minimize the cost of drone flying systems so that it can be accessible to a more massive crowd. The technological design behind the drone has been discussed in detail with mathematical equations. Also the control aspect has been presented in this work. For comparative analysis three drone have been designed and their performance have been compared.FindingsThe multi drone is designed , modelling is done and implemented in simulation and hardware. Its having less weight and cost compared to existing drone models.Originality/value75% original, 25% of the basic clarifications are taken from existing works.

It is essential to understand the quality of power being delivered to the industries that have equipment sensitive to power disturbances. Implementing IoT based power quality monitoring systems is a widespread practice in industries to prolong the process without any interruption. IoT based system looks very costly due to the data accumulation process which is carried over a long period requiring large data storage. Hence the paper proposes an intelligent power quality algorithm that alters data accumulation process more efficient by managing the power quality event data logging with pre-and-post value. The proposed system logs the drive’s mechanical quantities along with the power quality event data in Firebase cloud, which limits the space requirement and save time in analyzing the power quality events. Moreover, the power quality standards could be chosen by the end-users. Overall, the developed system is faster, economical and accurate and easier to operate, which can be used for many real-time applications.

Objective: The objective of the present study is the evaluation of the effect of the sublethal (40% and 60% of 48h LC50) binary combination (1:5 ratios) of molluscicides deltamethrin+MGK-264 on the endogenous levels of protein, amino acid and nucleic acid in different tissues of snail Lymnaea acuminata. Methods: The snails were treated with 1:5 mixtures of sub-lethal concentration of (40% and 60% of 48h LC50) deltamethrin+MGK-264 on the protein, amino acid and nucleic acid levels in gonadal, nervous and foot tissue of L. acuminata. In order to study the effect of withdrawal from treatment, the snails were first exposed to the above concentrations for 96h, after which they were transferred to freshwater. Water was changed every 24h for the next seven days, after which different biochemical parameters were estimated. Results: There was a significant change in the levels of protein (sublethal concentration of 60% of 48h LC50 after 96h) gonadal, nervous and foot tissues are 48.1, 12.1 and 14.5%, respectively, amino acid are 273, 234 and 252%, respectively, DNA are 25.1, 38.9 and 42.1%, respectively and RNA are 12.2, 30.7 and 30.5%, respectively. These changes were time and concentration-dependent. In the withdrawal experiment, the snails were treated for 96h to transfer in freshwater for 7 d, which caused significant recovery in all the biochemical parameters. Conclusion: The present study concluded that the high molluscicidal activity of deltamethrin+MGK-264 simultaneous decrease in the levels of proteins, DNA, RNA and increase in the level of amino acids.

Drive system is an assembly of various components such as source, power converter, machine, sensors and controller. Power converters of any drive system are one of the most vulnerable and important failure causes. The reliability of power converters plays a significant role in the uninterrupted operation of drives, especially in critical electrical drive applications. Since the solid-state switches are the most sensitive component in power converters, a reliable fault detection method is essential to provide uninterrupted service for any critical drive systems. This chapter assesses various fault identification methods and focuses on a method to detect the fault. When the fault is detected, the redundant switch is interposed to a power converter for improving the reliability of critical electrical drive applications. Redundancy has been widely employed in many safety-critical electrical drive applications such as space machines, aerospace, defense, electric vehicle and many other industrial applications. The type of source (AC/DC), machine model (IM, SRM, BLDC, PMSM) and converter configurations (AC–DC, DC–DC, DC–AC and AC–AC) are selected by the desired application. This chapter focuses on high-frequency signal-based fault diagnosis strategy for open-circuit (OCFs) and short-circuits fault (SCFs) identification on two-level three-phase voltage source power converter-based PMSM drive system. High-frequency current may cause temperature rise in the power converter circuits due to overheating. Hence, this chapter also focuses on identifying the switches with suitable materials and methods of cooling the parts which are prone to be melted and cause an electric fire. Materials that work under extreme temperatures, pressures and humid conditions are proposed for crucial components to ensure performance with high reliability under different operating conditions. In place of the conventional techniques that use extra sensors, the chapter focused diagnostic strategy identifies the faults from the high-frequency current components by inserting a high-frequency voltage signal into PWM signal of the inverter circuit. A reconfigurable two-level inverter presented for the reliability improvement of PMSM drives with control strategies. Both open-circuit (OCFs) and short-circuit faults (SCFs) are analyzed for different fault condition like single fault or hybrid fault for different drive speeds. Thus, any fault that occurs in the inverter circuit will not affect the performance of the electrical drive application, drive continuously runs with the same torque and speed even after fault. This developed system is implemented and validated in Typhoon Hardware-in-Loop environment.

Sensors are the foundational components of any smart machine system and are invaluable in all modern technologies. Consequently, faults and errors in sensors can have a significant negative impact on the setup. Intelligent, lightweight, and accurate fault diagnosis and mitigation lie at the crux of modern industries. This study aimed to conceptualize a germane solution in the domain of fault detection, focusing on Hall-effect rotary encoders. Position monitoring through rotary encoders is essential to the safety and seamless functioning of industrial equipment such as lifts and hoists, and commercial systems such as automobiles. This work used multi-strategy learners to accurately diagnose quadrature and offset faults in Hall-effect rotary encoders. The obtained dataset was then run through a lightweight ensemble classifier to train a robust fault detection model. The complete mechanism was simulated through interconnected models simulated in a MATLAB Simulink™ environment. In real time, the developed fault detection algorithm was embedded in an FPGA controller and tested with a 1 kW PMSM drive system. The resulting system is computationally inexpensive and achieves an accuracy of 95.8%, making it a feasible solution for industrial implementation.

The present work considers the task of forecasting the GHI for 1-11h ahead forecast horizon from the previous GHI time-series data. Dealing with the non-stationary and non-linear time series is a challenging and vital part of time-series forecasting. This paper aims to forecast the GHI, comparing the performance of two time-series decomposition techniques, i.e., EMD and EEMD. These techniques divide the original time series into a set of orthogonal series, which are termed as Intrinsic Mode Functions (IMFs). Further, the problem is converted into a supervised learning problem (input-output problem). A feature selection algorithm selects the highly correlated lag variables to reduce the complexity of the model. Finally, the forecasting is performed by utilizing machine learning algorithms i.e., Support Vector Machine (SVM) and ensemble learning-based Random Forest (RF). The parameters of ML algorithms are optimized for each IMF, and the final result is obtained by summing all the forecasted IMFs. This work also discusses the annual and seasonal variation of the forecasting model’s performance.

Employing switching sector based direct torque control on electric vehicle (EV) drive is a typical approach. Direct torque control (DTC) has improved torque response in controlling the induction motor drive, at the same time, the torque ripple is also highly generated. This nonlinear variation could be regulated through various control techniques. In this paper, an artificial neural network (ANN) based speed controller is deployed along with torque reference limiter. The proposed ANN controller is mainly preferred to reduce torque ripple and to enhance the steady-state and dynamic performances of the drive system. The ANN based DTC is implemented with 270 kW induction motor drive system and simulated to verify the results. The simulation of the EV model has been developed through integrating Typhoon hardware-in-loop tool in the Matlab Simulink environment.

We describe the fabrication and characterization of novel ionizing radiation detectors using wide band gap semiconductors like GaN and synthetic diamond. We have made interdigitated GaN metal-semiconductor-metal (MSM) detector with finger spacing of 8 \(\upmu \)m with Schottky junctions on GaN epitaxial layers. Synthetic diamond grown by microwave plasma-assisted chemical vapor deposition (CVD) is a high-speed detector with applications in high radiation environment. In this paper, we present measurements of current-voltage (I-V) characteristics, charge transport with transient current technique (TCT) and charge collection efficiency measured on GaN and single crystal CVD diamond prototype detectors.

The demand for electrical power is now increasingly growing day by day, which results in the transition of enormous power by increasing the ability of the transmission line from one location to another. As a result of the development of the structure of the power grid, detecting power system transmission line faults every year becomes a challenging task as the system becomes more complicated. Controlling and monitoring of power systems are inevitable due to the constant growth of the power system network. In order to find the correct decision, to search for any possible problems and fault conditions in a broad interconnected network, modeling and analysis of the power system have become significant. On the overhead transmission line, a fault will rise while the two or more of transmission lines touch each other. Generally, the faults in overhead transmission lines can be categorized as the line to ground fault, the line to line fault, double line to ground fault and three-phase faults. Single line ground faults mostly occur in the distribution network. The main purpose of this work is to study or explore the single line to ground fault and recognize the impact of the transmission line fault along with the industrial load under different loading conditions that is joined with transmission line according to the distance variation using Typhoon HIL.

This chapter focuses on developing a sustainable architecture for public electric motorbike charging stations. Electric motorbikes or electric bicycles (both referred to as e-bikes) are compact electric vehicles which are primarily battery-powered and driven solely by electric motors. This work conceptualizes a microgrid architecture which utilizes the integration of distributed generation energy resources providing the charging station nodes with sustainable power and increased fault tolerance. The charging stations proposed in the study increase the long-time energy savings of the infrastructure maintenance authorities while also reducing reliance on the public grid during peak hours. The photovoltaic-based DC microgrid is integrated with e-bike charging infrastructure, moving towards a future of eco-friendly and power-efficient technology.