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The emergence of environmental-friendly electric vehicles (E-Vehicles) presents a need for high-static gain DC-DC converters. One of the more popular DC-DC converters is the Single-Ended Primary-Inductance Converter (SEPIC), often used in applications where DC-DC conversion is needed; this is because the SEPIC allows for either step-up or step-down conversion. However, the SEPIC is usually not a practical solution for high-voltage applications due to the limited voltage gain. This paper presents a new DC-DC converter named the Primary-Switched-Inductance Single-Ended Converter (PSI-SE Converter) suitable for low to high-voltage boosting applications. The topology of this converter is mainly derived from the SEPIC topology alongside some minor modifications in the switched-inductor network, where the two outer diodes in the switched-inductor structure are replaced with capacitors. The operation of the PSI-SE converter is discussed thoroughly with mathematical analysis and compared to other similar converters. The simulation results are presented to validate the performance of the converter. The proposed topology is the solution for low to high-voltage boosting in E-Vehicles applications.

A hysteresis motor is an inherently synchronous motor in which accuracy is more important than efficiency. The rotor is a smooth cylinder type made up of permanent magnet material having high hysteresis loss in its simplest form. We use a hysteresis ring around a non-magnetic material to reduce the rotor's weight, say aluminum. Rotor ring material handles the efficiency of the motor to some extent. This paper involves designing a three-phase hysteresis motor using the scrap material for rotor ring, i.e., hard magnetic material, a composition of aluminum, cast iron, and Nichrome, and provide an alternative to the material currently used for manufacturing hysteresis motor. Further, this paper proposes to reduce the motor's size and improve the hysteresis motor's efficiency.

Enhancing the reliability of Multi-input converters (MICs) with fault tolerance plays an important role while integrating with independent energy sources with any significant loads. In literature, many MICs are proposed and operated on the time-multiplexing concept. Only one energy source contributes to the loads, and others are idle during a particular duty interval. This leads to the underutilization of the converter as well as the input energy sources. A new configuration of Dual-Input and Single Output (DISO) DC-DC converter with switch and source fault tolerance has been proposed in the proposed work. The fault tolerance is the open-circuit condition due to the power switch fault and against one input short-circuit. The designed converter can deliver the buck, boost, and buck-boost operations. The feasibility of the proposed scheme, a 200W prototype is designed, and experimental results are used for validation.

In recent times, important contributions and the incorporation of renewable sources, such as photovoltaic, fuel cells, etc., are the main reasons for the popularity of DC microgrids. Integrating renewable sources into the microgrid requires high-voltage, high-efficiency DC-to-DC converters. Without modification, traditional converters are not suitable for achieving the required voltage in microgrid applications, due to the requirement for a large duty cycle, inductor resistance, voltage rating of switches, reverse recovery of the diode, high current rating inductors, etc. Various converters based on the circuitry of voltage multipliers, switched inductors/capacitors, coupled inductors, transformers, etc., have been proposed in the literature and have their drawbacks. In this paper, to realize significant voltage gain with two duty cycle controls, a new multi-leg (ML) converter is recommended as a solution for use in DC microgrids. The converter is designed by incorporating multiple legs into the boost converter. The implications of the proposed converter are reduced device voltage stress, small inductors and capacitors, two-duty cycle control, triple mode operation, single-stage conversion, etc. The proposed converter’s power circuits, mode of operation, and design equations of the converter are presented. The non-ideal model of the proposed converter is discussed, and efficiency is analyzed. The effect of unequal insurance on the operation of the converter is discussed. Comparative studies of converters are provided to draw attention to the benefits of the converter. The results of the experiments are shown to prove that the analysis and performance of the converter are correct. The discontinuous mode of operation and unequal inductance case of the converter are studied with the help of a simulation model.

Developing of new photovoltaic inverter topologies is received more attention in the last few years. In particular, designing an active neutral-point-clamping inverter type structure is quite popular for PV applications. The output voltage is always half of the input voltage (vin), which further increases the voltage rating of dc-link capacitors in the conventional three-level ANPC. To rectify the above problem and increase the output voltage by reducing dc-link capacitors voltage rating, a new boost type seven-level ANPC inverter topology is proposed. The proposed topology consists of seven switches and one floating capacitor. The floating capacitor voltage is self-balanced, and the output voltage is 1.5 times higher than the input voltage. A detailed comparison for some power components, power loss and cost with other existing topologies are presented. Further, the proposed topology is validated in a prototype hardware setup for different load values.

This paper presents the use of a delta-sigma quantizer for generalized one-bit control processing. An equivalent control strategy based on sliding-mode control is employed to derive the necessary condition for the convergence of the proposed one-bit control system in both the continuous-time and discrete-time domains. Under the convergence condition, the binary signals generated by delta-sigma quantizers in the one-bit control system effectively replace their counterpart signals in conventional control systems. This enables a significant reduction in the number of multipliers and overall hardware cost for computing the control laws in one-bit control systems. Our result is applied to design a multiplier-less one-bit generalized proportional and integral controller for the position control of an experimental prototype of a DC motor. An implementation of the one-bit control system is carried out using an FPGA platform to demonstrate the behavior of one-bit generalized proportional and integral controller and compare the results with the standard in terms of implementation efficiency. The results of the simulation and experiment show that the one-bit generalized proportional and integral controller effectively controls the system and achieves the desired specifications. At the same time, the proposed one-bit control system consumes significantly fewer hardware resources than the standard control system.

The main attention of this paper is to tackle the issue of robust tracking and disturbance estimation for the four wheel steering vehicle by implementing a feedback control design based on the improved-equivalent-input-disturbance technique and the Smith predictor approach. The intention of incorporating the Smith predictor into the controller design is to compensate the effect of input time-delay. In addition, the improved-equivalent-input-disturbance technique is applied for dealing with the nonlinear uncertainty and external disturbances. Based on the proposed controller, both front and rear steering angles of the considered model are controlled to trace the desired position of the vehicle. More precisely, the developed criterion that ensures the desired tracking performance of the vehicle is obtained in the form of linear-matrix-inequalities based on the construction of an appropriate Lyapunov-Krasovskii functional candidate. Numerical simulation results for vehicle dynamics are presented to examine the performance of the proposed control strategy.

Modular Multilevel Converters (MMC) are gaining importance because of their flexible structure, re-configurable property, and simplicity of operation. The operation of MMC at a low-switching frequency (LSF) helps in enhancing the performance of the converter. This paper proposes an improved harmonics mitigation scheme for a Multilevel DC-Link Inverter (MLDCLI), which is a variant of MMC. The proposed scheme is a modified version of the conventional Nearest Level Modulation (NLM) scheme, termed as modified Nearest Level Modulation (mNLM) Scheme. The proposed scheme is effective compared to NLM because of the choice of the switching angles obtained by the use of the algorithm proposed. The MLDCLI topology is operated for twelve different configurations, and mNLM is implemented on all the configurations. Using MATLAB software, the simulation results are validated, and the same is extended to a hardware prototype. The simulation and experimental results of NLM and mNLM schemes are compared. The effectiveness of the proposed scheme is evident by the reduced voltage THD, increased rms voltage, increased rms current, and increased output power.

The present study proposes a new dynamic two-level quantizer, called as hybrid delta modulator ( $\Delta _{H} $-M), which combines the features of both delta-modulator and delta-sigma modulator. In the transient state, the $\Delta _{H} $-M exhibits the dynamical behaviour of delta modulator (Δ-M) while in steady state, its behaviour is similar to delta-sigma modulator ( $\Delta \Sigma $-M). This study investigates about various dynamics of the proposed $\Delta _{H} $-M in both continuous and discrete-time domains. The stability conditions of $\Delta _{H} $-M are derived using the theory of sliding and quasi-sliding mode. The theoretical results are validated through extensive simulations. Abbreviations: Δ-M: delta modulator; $\Delta _{H} $-M: hybrid delta modulator; $\Delta \Sigma $-M: delta sigma modulator; ADC: analogue to digital converter; CT: continuous-time; DAC: digital to analogue converter; DT: discrete-time; NCS: networked control system; QSM: quasi-sliding model; QSMD: quasi-sliding mode domain; SS: steady state; TP: transient process

The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through analysing the CLOs’ performance by using an advanced analytical model (i.e., the Rasch model) in the context of engineering and business education. This model produced an association pattern between the students and the overall achieved CLO performance. The sample in this project comprised students who are enrolled in some nominated engineering and business courses over one academic year at Prince Sultan University, Saudi Arabia. This sample considered several types of assessment, such as direct assessments (e.g., quizzes, assignments, projects, and examination) and indirect assessments (e.g., surveys). The current research illustrates that the Rasch model for measurement can categorise grades according to course expectations and standards in a more accurate manner, thus differentiating students by their extent of educational knowledge. The results from this project will guide the educator to track and monitor the CLOs’ performance, which is identified in every course to estimate the students’ knowledge, skills, and competence levels, which will be collected from the predefined sample by the end of each semester. The Rasch measurement model’s proposed approach can adequately assess the learning outcomes.

Multilevel inverters play an important role in extracting the power from renewable energy resources and delivering the output voltage with high quality to the load. This paper proposes a new single-stage switched capacitor nine-level inverter, which comprises an improved T-type inverter, auxiliary switch, and switched cell unit. The proposed topology effectively reduces the DC-link capacitor voltage and exhibits superior performance over recently switched-capacitor inverter topologies in terms of the number of power components and blocking voltage of the switches. A level-shifted multilevel pulse width modulation scheme with a modified triangular carrier wave is implemented to produce a high-quality stepped output voltage waveform with low switching frequency. The proposed nine-level inverter’s effectiveness, driven by the recommended modulation technique, is experimentally verified under varying load conditions. The power loss and efficiency for the proposed nine-level inverter are thoroughly discussed with different loads.

Microgrids are often considered as the solution for affordable and clean energy in the distribution sector. This article presents the small-signal stability analysis of a distributed generation unit in an autonomous microgrid operation. The purpose of the proposed strategy is to optimally improve the capacity of the power system to restore the reasonable operating condition following a small physical disturbance. The proposed strategy is the joined execution of both the modified antlion optimization algorithm (MALO) and artificial neural network (ANN), and hence it is abbreviated to MALANN. In this article, the proposed controller comprises two control loops, namely the inner current control loop and the outer power control loop. The MALO technique is incorporated to generate the dataset of possible proportional integral (PI) gain parameters. By using the accomplished dataset of MALO, the ANN is trained, and convincing estimate execution is brought out through the entire machine working condition. The proposed strategy is implemented in MATLAB/Simulink, and the results are examined with two test cases and compared with various solution techniques such as base method and ant-lion optimization. The results prove that the stability analysis is reasonably accurate, and the controller offers a reliable system's operation.

This paper presents a new multilevel converter with a reduced number of power components for medium voltage applications. Both symmetric and asymmetric structures of the presented multilevel converter are proposed. The symmetric topology requires equal dc source values, whereas the asymmetric topology uses minimum switch count. However, both structures suffer from high blocking voltage across the switches. To reduce the blocking voltage on switches, an optimal topology is presented and analyzed for the selection of the minimum number of switches and dc sources, while maintaining a low blocking voltage across the switches. A comparative analysis with recently published topologies was performed. The simulation results, as well as the comparative analysis, validated the robustness and effectiveness of the proposed topology in terms of the reduced power loss, lowered number of components, and cost. Furthermore, in addition to the simulation results, the performance of the proposed topology was verified using experimental results of 9, 17, and 25 levels.

The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

The power supply from conventional sources is not adequate because of the increasing demand for electricity, and this has increased the need for renewable energy sources. Even though integration of the renewable energy resources reduces the gap between generation with demand, the emergence of the smart grid makes the whole power system sicker in terms of the quality of power. One of the most exciting technologies producing benevolent electricity is the photovoltaic (PV) system. Building applied photovoltaic system and building integrated photovoltaic system are two separate ways of integrating solar PVs into a commercial or domestic building. BIPV methodology is considered as case study in this chapter. A grid-tied BIPV system has been designed for an automobile showroom which is governed to act as an active filter to provide reactive and harmonic compensation for the loads at the system's coupling point with a proper filtering algorithm. The voltage fluctuation issue due to the indeterminant nature of the solar PV has been addressed by a proper battery management system, and the results in terms of active power sharing and total harmonic distortion comparison were analyzed using MATLAB/Simulink.

In modern days, to achieve low emission of Green-House Gases (GHG), clean system, easy maintenance, high starting torque, and high efficiency, Internal Combustion (IC) engines are gradually being changed by electric motors. Smart vehicular systems (SV) either functioned from moderately or fully electrical energy generated from renewable sources. The energy storage, i.e., battery or ultra-capacitor system, gains energy from the IC engine. Alternative the electric grid, e.g., IC engines and electric grids are used to charge the battery in Hybrid Smart Electric Vehicle (H-SEV) and Plug-in Smart Electric Vehicle (PSEV), respectively. Power Electronics skills play an imperative role in the energy conversion between Grid-vehicle-Wheel (G-VW) and Wheel-vehicle-Grid (W-V-G). This letter discusses smart vehicular systems with their demand, curiosity, challenges, and power electronic skills.

A new single-phase asymmetrical multilevel inverter (MLI) is presented in this study. The proposed topology generates a staircase output voltage waveform with a maximum number of levels using less number of components compared to several existing and recent topologies. The basic module consists of a combination of two isolated DC sources with ten switches that produce all the possible number of levels. Other advantages of the proposed MLI include improved output voltage performance and a low blocking voltage of the switches. The low switching frequency pulse width modulation (LSF-PWM) technique has been used for the generation of gate pulses. In the LSF technique, selective harmonic elimination (SHE) and fundamental switching frequency PWM techniques have been discussed for the better output voltage waveform. The optimised switching angles with SHEPWM has been calculated using particle swarm optimisation considering the different combination of the elimination of lower order harmonics. Simulation work was carried out using MATLAB/SIMULINK, and a prototype was implemented to validate the proposed MLI module. Simulation and experimental results have been provided in the study to show the performance of the proposed topology with these modulation techniques.

For multilevel inverter topologies, the boosting feature is an important aspect for the application related to the low voltage input obtained from solar PV for high voltage application. The proposed RSC-BI aims for the higher voltage gain with lower device count. The proposed RSC-BI uses eight switches with two capacitors for seven-level (7L) waveform having triple voltage gain. In this paper, a detailed comparison has been included along with the simulation and experimental results. All these results and discussion shows the better performance of the proposed RSC-BI.

To mitigate the voltage transfer gain issue of the classical power converters, an isolated boost converter can be a solution. However, it increases the power circuit dimension and expense due to the involvement of many coupled inductors and transformer. In this treatise, a 3Nx DC-DC converter is proposed for high voltage gain application. High voltage transfer gain is obtained by suggested converters and circuitry is derived from two multistage converters (one inverting and second non-inverting) by connecting them. In more specific, 3Nx power converter fusing the characteristics of 2Nx power converter and Nx DC-DC converter, where voltage multiplier and switched inductor cells are used to improve voltage gain. By doing this, the 3Nx power converter generates the voltage transfer gain 3N times higher than the simple boost power converter, where N is several stages in the suggested power converter. The magnitudes of DC voltage can be increased with more number of stages to make renewable energy resources appropriate at the user level. The operation, voltage gain derivation, comparison with recent converter, simulation study are given. The obtained results are closely matched with mathematical analysis and approve the operation of the 3Nx power converter.

In this brief, a new cross-connected compact switched capacitor (C3SC) cell is introduced for multilevel inverter applications. The proposed CCS cell uses four switches and two diodes for interconnecting the input dc source and floating capacitors (FCs). A nine-level inverter is derived with the proposed C2SC cell requiring only ten switches and two FCs. The proposed C3SC cell-based MLI is self-balancing and has a voltage gain of two. All the switches of the proposed topology have a maximum blocking voltage within the input dc voltage (v $_{in}$ ) value. The operating principle is detailed, and a simple logic gate based gate pulse generation scheme is presented. Detailed simulations and experimental results obtained from an 850 W prototype with several test cases are presented to validate the operation of the proposed topology. Finally, a detailed comparative assessment is performed with other recent SCMLIs to demonstrate the merits and superiority of the proposed topology.

Usually, low magnitude voltage is generated from the fuel cell. For obtaining high voltage for electric vehicle application, a series connection of many fuel cells (FCs) becomes impractical. Thus, for multiple high voltage supplementary loads, DC-DC converter having the ability of high voltage transformation ratio and providing multiple outputs is desired. This article formulates an innovative configuration called 2L-L triple port converter, supplying higher voltage supplementary loads of fuel-cell automobiles. The details about concept and block representation for the proposed converter are delivered with their switching conditions and distinctive waveforms. The proposed 2L-L triple port converter different operating modes are detailed, and the voltage transformation ratio in these modes is examined. Additionally, the suggested 2L-L triple port circuitry advantage over existing circuitries is also discussed. The suggested converter's operation is tested by software model and attained outcomes confirm the theoretical analysis.

This research work demonstrates a bio-inspired chicken swarm optimization (CSO)-based maximum power point trackers (MPPT) for fuel cell-based electric vehicle applications. The natural clamp zero current switching-based bi-directed converter has been used which eliminates the requirement of passive snubber components which reduces the switching losses. The proposed model works on the hierarchal sequence of swarm chicken and its action which can be utilized for utilization of fuel cell fed electric vehicle. The proposed CSO-based MPPT provides optimal fuel cell power with high accuracy.

In this study, an active neutral point clamped-type boosting switched-capacitor multilevel inverter (SCMLI) with self-voltage balancing capability is proposed. In the proposed topology, a novel switched capacitor cell is used, which has eight switches and two diodes. The presented topology has reduced power component count with self-boosting and balancing abilities. The distinctive features of the proposed topology are highlighted and benchmarked against other recent 7L-SCMLI topologies. To validate the feasibility of the proposed topology, experimental tests are performed on a 1 kW prototype hardware setup.

During the past two decades, a new paradigm in control engineering, called networked control system (NCS), has emerged. This has been shown to be a successful alternative to traditional control systems. The present study designs a reinforcement learning controller based on the concept of Dynamic Fuzzy Q-Learning (DFQL). This controls the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) of a wind energy conversion system (WECS) through a communication network. A comparative performance investigation has been carried out with two other controllers; namely a conventional PID and Q-Learning controller. The performance of these controllers is studied considering various imperfections of the communication network such as random delay and packet dropout. Simulations are carried out considering a WECS consisting of 20-DFIGs of 5MW capacity each. The results of the study demonstrate that the DFQL controller shows better performance compared to Q-Learning and conventional PID controllers.

Nowadays, power quality becomes an essential concern in the field of power system networks due to the use of sensitive devices. The problem associated with power quality is voltage sag, damage to power equipment, product quality degradations, voltage swell, etc. But one of the severe disturbances in the power system is the problem of voltage sag. To mitigating these disturbances, the use of custom power devices or FACTS devices with the help of power electronic devices is presented. Hence an economical and proficient power device called as the Series Compensator Circuit (SCC) is employed in power distribution networks. This device helps in injecting the voltage in synchronism with the standard system voltage to compensate the voltage influences in the situation of voltage disturbance. This article deals with modelling, methodology and simulation of a Series Compensator Circuit (SCC). A simulation is modelled using MATLAB/Simulink. The simulation results are presented, which validates the functionality and the concept effectively.

In DC microgrid, the combination of energy source, especially renewable with the unit for storage and utility load rely heavily upon the electronics power converters. This paper proposes a novel DC to DC converter called Quad-Switched-Inductor Double-Switch (QSI-DS) converter for microgrid voltage step-up applications. The non-isolated topology utilizes Quad-Switched-Inductors network to develop to proposed converter configuration to achieve high voltage gain. Two switches used to control the flow of energy from input to the output of the converter. Reduced voltage stress across the switches and high gain are the merits of the proposed converter in this work. Principle of operation for continuous, and discontinues modes of the suggested converter, also elaborated. Analysis for gain calculation, comparison with recently suggested converters is presented. The theoretical analysis and performance of the suggested topology of converter validated through Simulink model.

This paper proposes an improved “K” type seven‐level switched capacitor inverter topology. The proposed topology consists of eight active switches, one floating capacitor, and two dc‐link capacitors. The floating capacitors are charged and discharged in each half‐cycle to maintain the capacitor voltage with the same value of the input voltage. The floating capacitor voltage is self‐balanced, and the output voltage is 1.5 times higher than the input voltage. To prove the superiority of the proposed topology, it is compared with the existing seven‐level inverters in terms of maximum voltage stress on the switch and required the number of power components. The simulation and experimental validation are carried out for 1.6 kW, 50 Hz using a laboratory‐based setup with a switching frequency of 5 kHz. The results are discussed highlighting the performance of the proposed topology for the dynamic load variations at different modulation indices. Both simulation results and experimental results have good agreement in terms of voltage balancing of flying capacitor.

Highly efficient passive Light Emitting Diode (LED) driver configurations are studied and examined in this paper. The technology based on the LED becomes a promising lighting technology to exchange inefficient energy lamps. For outdoor applications, the LED drivers based on passive elements are appropriate in extreme weather conditions. A comparison of LED driver configurations is presented. Each LED driver configuration is validated through simulation, and their waveform characteristics are examined and analyzed.

With the advent of distributed generation (DG), the need for efficient power electronic converter is increasing as the incorporation of renewables with battery unit and loads rely heavily on them. This paper suggests an original structure of high-gain DC to DC converter named as 'Two-Tier converter' with the reduction in voltage stress. The configuration is resultant of stacking two-stage of the classical boost converter. The voltage gain offered is high for even lower values of duty cycle as the relation of gain is quadratic. Also, the converter current at the input is continuous, and the load configuration offered is grounded. Analysis and operational modes of the proposed converters and effect of the effective series resistance of inductors on voltage gain are presented in detail. A comparative analysis of suggested and new high-gain converters is presented in detail. The suggested configuration is confirmed using detailed simulation studies.

In significant cases, the generated voltage needs to be step-up with high conversion ratio by using the DC-DC converter as per the requirement of the load. The drawbacks of traditional boost converter are it required high rating semiconductor devices and have high input current ripple, low efficiency, and reverse recovery voltage of the diodes. Recently, the family of Multilevel Boost Converter suggested and suitable configuration to overcome the above drawbacks. In this article, hybrid DC-DC non-isolated and non-inverting Nx Interleaved Multilevel Boost Converter (Nx-IMBC) is analyzed in Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM) with boundary condition and investigated in detail. The Nx-IMBC circuit combined the features of traditional Interleaved Boost Converter (IBC) and Nx Multilevel Boost Converter (Nx-MBC). The modes of operation, design of Nx-IMBC and the effect of the internal resistance of components are presented. The comparison study with various recent DC-DC converters is presented. The experimental and simulation results are presented with or without perturbation in input voltage, output power and output reference voltage which validates the design, feasibility, and working of the converter.

Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size.

In remote locations such as villages, islands and hilly areas, there is a possibility of frequent power failures, voltage drops or power fluctuations due to grid-side faults. Grid-connected renewable energy systems or micro-grid systems are preferable for such remote locations to meet the local critical load requirements during grid-side failures. In renewable energy systems, solar photovoltaic (PV) power systems are accessible and hybrid PV-battery systems or energy storage systems (ESS) are more capable of providing uninterruptible power to the local critical loads during grid-side faults. This energy storage system also improves the system dynamics during power fluctuations. In present work, a PV-battery hybrid system with DC-side coupling is considered, and a power balancing control (PBC) is proposed to transfer the power to grid/load and the battery. In this system, a solar power conditioning system (PCS) acts as an interface across PV source, battery and the load/central grid. With the proposed PBC technique, the system can operate in following operational modes: (a) PCS can be able to work in grid-connected mode during regular operation; (b) PCS can be able to charge the batteries and (c) PCS can be able to operate in standalone mode during grid side faults and deliver power to the local loads. The proposed controls are explained, and the system response during transient and steady-state conditions is described. With the help of controller-in-loop simulation results, the proposed power balancing controls are validated, for both off-grid and on-grid conditions.

Sliding mode quantisers (SMQs), where signals are converted to switching signals represented by the sequence of binary values −1's and +1's, are increasingly being used in various applications incl...

This paper proposes an $H_{\infty }$ robust state-feedback controller design for uncertain Takagi–Sugeno fuzzy systems using a nonmonotonic Lyapunov function. In the nonmonotonic approach, the monotonicity requirement of the Lyapunov function is relaxed by allowing it to increase locally. Based on the nonmonotonic Lyapunov function approach, sufficient conditions for the existence of a robust state-feedback $H_{\infty }$ controller that guarantees stability and a prescribed $H_{\infty }$ performance are given in terms of linear matrix inequalities. The proposed design technique is shown to be less conservative than the existing $k$ -samples variations of the Lyapunov function. The effectiveness of the proposed approach is further illustrated via numerical examples.

This paper deals with the problem of designing a controller for a class of discrete-time nonlinear systems which is represented by discrete-time polynomial fuzzy model. Most of the existing control...

Fault tolerant control of affine class of multi-input multi-output (MIMO) nonlinear systems has not received considerable attention of researchers compared to other class of nonlinear syste...

This brief proposes a novel adaptive two-level quantizer, which generates 1-b per sample and can be used for a class of industrial networked control systems (NCSs). The adaptive quantizer is introduced to automatically estimate the input of the quantizer using equivalent control-based sliding mode control. The stability condition of the proposed quantizer is derived analytically with the help of a sliding-mode analysis. The effectiveness of the proposed quantized NCS is illustrated experimentally by designing a quantized feedback controller to control the position of a dc motor, where signals with 1-b per sample are utilized to interconnect the controller with the plant. Simulation results from a laboratory prototype illustrate that the proposed quantizer with the feedback controller controls the system effectively, introduces less quantization noise, and minimizes the data rate.

Bidirectional Inductive power transfer (IPT) systems behave as high order resonant networks and hence are highly sensitive to changes in system parameters. Traditional PID controllers often fail to maintain satisfactory power regulation in the presence of parametric uncertainties. To overcome these problems, this paper proposes a robust controller which is designed using linear matrix inequality (LMI) techniques. The output sensitivity to parametric uncertainty is explored and a linear fractional transformation of the nominal model and its uncertainty is discussed to generate a standard configuration for μ-synthesis and LMI analysis. An H∞ controller is designed based on the structured singular value and LMI feasibility analysis with regard to uncertainties in the primary tuning capacitance, the primary and pickup inductors and the mutual inductance. Robust stability and robust performance of the system is studied through μ-synthesis and LMI feasibility analysis. Simulations and experiments are conducted to verify the power regulation performance of the proposed controller.

The classical method of perturb and observe (P&O) is mostly utilized because of it is simple technique, ease of implementation and low cost. Although, it has high oscillations around maximum power point (MPP) at steady state due to perturbation and challenge between step size and the convergence time. To avoid the drawbacks of classical P&O methods, this paper presents adaptive P&O using optimized proportional integral (PI) controller by particle swarm optimization (PSO) algorithm. To evaluate the proposed method, a PV system model is designed with different scenarios under various weather conditions. For each scenario, simulations are carried out and the results are compared with the other classical P&O MPPT methods. The results revealed that the proposed PSO-PI-P&O MPPT method improved the tracking performance, response to the fast changing weather conditions and also has less oscillation around MPP as compared to the classical P&O methods.

Pumps play a pivotal role in both energy and water conservation. They account for the 20% of the world's total energy consumption and thus monitoring it becomes more relevant to decrease an energy wastage. The performance of the pump deteriorates for various reasons, such as cavitation, sedimentation of silt and water hammering, electrical and mechanical faults. Performance of the pump under silt-laden and identification of silt is seldom studied. It causes severe damage in the pumping system. Identification of sludge particles can productively result in energy savings, water conservation, and energy efficiency. With the advent of machine learning and artificial intelligence, pumps can incorporate these methods to become self-reliant in the identification of type and concentration of silt while pumping the fluid. Machine learning is a modern advanced technology, which leads to predict the anomalies of the machine in ground level. This paper presents the how to identify and predict sludge problem in water pumping system using machine learning algorithm.

Power inverters produce common mode voltage (CMV) and common mode current (CMC) which cause high-frequency electromagnetic interference (EMI) noise, leakage currents in electrical drives application and grid-connected systems, which consequently drops the efficiency of the system considerably. This CMV can be mitigated by designing suitable EMI filters and/or investigating the effects of different modulation strategies. In this paper, the effect of various modulation techniques over CMV and CMC are investigated for two-level and three-level inverters. It is observed that the modified third harmonic injection method reduced the CMV and CMC in the system by 60%. This modified pulse width modulation (PWM) technique is employed along with EMI chokes which results in reduced distortion of the system.

Optimal siting and sizing of Distributed generators (DGs) and capacitors helps in enhancing the overall performance of distribution system. A proficient method to optimally allocate DGs and shunt capacitors in distribution system is proposed in this paper. The major objectives of the work are power loss reduction, voltage profile improvement and operating cost minimization. A standard IEEE 33 bus test system with constant power load at various load levels is considered for the analysis. Optimal siting of DGs and capacitors is carried out using sensitivity analysis technique. Further Flower pollination algorithm is employed for optimal sizing of DGs and capacitors and the results are compared with few other noteworthy methods. Scopus

The present study designs a novel ΔΣ-based PID controller for controlling the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) based wind energy conversion system (WECS). The proposed controller essentially uses ΔΣ modulators to represent the analogue signals in single-bit which is ideally suited for FPGA or application specific integrated circuit implementation and offers numerous advantages due to its inherent parallel nature. The performance of the controller is compared with various controllers including a conventional PID, Q and Dynamic Fuzzy-Q learning (DFQL) based intelligent controllers. The results of the simulation on WECS consists of 20 wind turbines and 20 DFIGs, illustrate that the ΔΣ based PID controller could perform better compared to conventional PID and Q-learning based controllers.

During the past decade, networked control systems (NCS) has emerged as a viable alternative to traditional control systems due to various advantages it offers which include a reduction in system wiring, increase of system agility etc. However, the performance of various existing controllers such as PID degrades in the networked environment due to the existence of random time-varying delay, packet-dropouts which may cause instability. The present study designs a neural network (NN) based controller for NCS and investigates its performance under random time-varying delay, packet-dropouts. The performance of this controller is compared with both the classical PID and ΔΣ-based PID controllers. The robustness of the NN based controllers in the networked environment is studied under different degree of parametric uncertainties considering an example of a DC servo mechanism. The results of the comparative investigation demonstrate that the performance of the NN based controller is superior compared to other controllers.

Among the total energy consumed by presently installed utilities, pumping systems contributes nearly 30% of them that are controlled through induction motor. The flow rate of centrifugal pump is directly proportional to the speed of induction motor. Induction motor based electric drives are a complex nonlinear system to control due to their varying nature of speed with respect to changes in load. Thus, controllers are required for the speed control and performance enhancement of the induction motors. In this paper, a comparison of various controllers is discussed for the flow rate control of the pumping system. Among all the conventional controllers the proportional integral derivative (PID) is the mostly preferred for induction motor speed control. However, high overshoot, fixed gain constants and sluggish response are the disadvantages of the PID controller. So, automatic gain tuned PI controllers (i.e., adaptive PI controllers) are introduced to overcome the drawbacks of the conventional PID controllers. Further advancements in the field of artificial intelligence lead to the development of fuzzy logic based controllers. These controllers are simple, compatible and can be modelled without detailed mathematical model of the system. Thus, they are gaining more popularity than other controllers. The rules and membership functions (MFs) are responsible for performance of fuzzy logic controllers are designed using trial and error method. The result comparison of all the controllers is presented and the performance of each controller is analyzed. From the observations, fuzzy logic controller provides better performance than the conventional controllers.

Filtering is an important research issue, particularly in signal processing applications, as it provides the means to estimate state information in the presence of disturbance and system uncertainties. This chapter develops robust H ∞ output feedback filtering for uncertain Takagi–Sugeno (T-S) fuzzy systems via linear matrix inequalities (LMIs). Our main focus is to adopt the idea of nonmonotonic approach to further reduce the conservatism and improve the performance of the H ∞ output feedback filtering. An illustrative example is given at the end of this chapter to show the feasibility and efficiency of the developed method.

A Takagi–Sugeno (T-S) fuzzy model of the nonlinear system is often constructed as a combination of linear models with nonlinear scalar membership functions. This motivates researchers to design filters and controllers for nonlinear systems represented by T-S fuzzy systems using the linear matrix inequalities (LMIs) framework. However, many issues related to using LMIs fuzzy control, like conservatism, are still not completely solved. This chapter uses a nonmonotonic Lyapunov function (NLF) to design robust H ∞ state feedback controller for uncertain T-S fuzzy systems. In the nonmonotonic approach, the monotonicity requirement of the Lyapunov function (LF) is relaxed by allowing it to increase locally but go to zero ultimately. Based on the NLF approach, sufficient conditions for the existence of a robust state feedback H ∞ controller that guarantees stability and a prescribed H ∞ performance are given in terms of LMI. The design technique is shown to be less conservative than the existing k -sample variations of LF.