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Abstract This paper investigates the stability and reliability analysis of a non‐isolated high‐gain DC‐DC converter. The proposed DC‐DC converter produces a high gain output voltage with a lower duty ratio and reduced voltage stress on the semiconductor switch. A reliability analysis is performed to predict the failure rate and lifetime of the individual components using the military handbook (MIL‐HDBK‐217F). A state space equation and small signal modeling of the proposed converter are derived to elucidate the performance of the proposed converter through stability analysis. A closed‐loop using PID controller is incorporated to attain the constant regulated output voltage during sudden parameter changes. The validation of the closed‐loop using the PID controller is verified with the wide range of variations in the different parameters, such as input voltage, load value, and reference voltage value. The laboratory‐based prototype is developed for the proposed converter and validated the performance with 250 W. The power density and efficiency of the proposed converter are 1.360 kW/L and 93%, respectively.

This article proposes a new high-gain converter to attain higher output voltage without coupled inductors. The operation of the proposed converter is examined under continuous conduction mode (CCM) and discontinuous conduction mode (DCM). Also, the mathematical expressions for the design of inductors, capacitors, voltage and current stress of semiconductor devices are provided. The proposed converter is simulated in MATLAB-Simulink and validated the effectiveness of the performance using a laboratory-based Prototype of 250 W at 50 kHz with a 60% duty cycle. The experimental results of the individual components are matched with the simulated results. Due to the continuous input current, the proposed converter is suitable for photovoltaic (PV) applications.

Remote island power systems are difficult to install renewable energy sources (RES) because of the small system capacity and the large impact of intermittency of RES. Virtual synchronous generators (VSG) can equip inverters with the inertia of synchronous generators and can suppress frequency and voltage fluctuations. This paper proposes a VSG control method for dynamic voltage stability in remote island power systems. In conventional VSG control methods, the command of active and reactive power is determined by the Droop characteristics of frequency and transmission line voltage. However, the command are not based on dynamic voltage stability indices. Therefore, the compensation amount of active and reactive power is inappropriate. The proposed method determines the command of active and reactive power based on dynamic voltage stability indices. The dynamic voltage stability index considers the critical boundary index (CBI). The CBI is defined as the distance between the operating point of active and reactive power flowing through a transmission line and the stability limit curve calculated from the power flow between two bus lines. The proposed method improves dynamic voltage stability by determining power command so that the operating points of active and reactive power are closer to the origin direction. Simulation verification is performed for load increase and three-phase ground fault. In the three-phase ground-fault simulation, FRT requirements are considered to verify continued operation and the case of gate blocking. The conventional Droop control and the proposed method are compared for the case of continued operation with FRT requirements during load increase and ground fault. Simulation verification results show that the proposed method improves the dynamic voltage stability compared to the conventional method. Furthermore, it is shown that the proposed method is capable of stable continuous operation of the system even after a transmission line failure considering the FRT requirement.

In recent years, fault diagnosis in the Induction Motor Drive (IMD) has been a popular and important field in the motor-drive research area. The development of control circuits for induction motors has prompted the attention of both researchers and industrialists. This paper proposes a broken bar fault diagnosis using Hilbert Transform (HT) and Artificial Neural Networks (ANN), with the drive regulated through the Indirect Field Orientation Control (IFOC). The HT obtains the spectrum of stator current, which is utilized to identify the Broken Rotor Bar (BRB) failure. The magnitude and side-band frequency of the drive are extracted using the Fast Fourier Transform (FFT), and these parameters are fed into the ANN inputs. The fault severity is computed by the ratio of mean side-band frequency amplitude to the main frequency amplitude for finding the impact of failure in the drive. ANN is used to diagnose failure with high accuracy. The tested and training results are used to attain the minimum Mean Square Errors (MSEs). The IFOC is involved in this proposed system to ensure high performance under the variable speed drives. The proposed scheme is validated in both MATLAB/Simulink and experimental platforms.

Electrical microgrids (EMGs) are positioned to play an important role in the future distribution grid as technology advances and distributed energy resources (DERs) emerge. In the event of major natural disasters, the functioning capabilities of active distribution systems (DS) face ongoing problems. To assess the resilience of a distribution system, a thorough methodology must be established. This study proposes a methodology that demonstrates how the use of EMGs and DERs, in conjunction with line hardening, can improve resilience in extreme operating situations. The framework examines four separate scenarios, each with its own set of restoration procedures and critical loads. A combination of battery electric vehicles (BEVs), solar photovoltaic distributed generation (SPV-DG), battery energy storage systems (BESS), and distribution static compensators (DSTATCOMs) is being integrated into practical Indian distribution systems consisting of 28 and 52 buses to improve resilience. The goal is to improve the newly specified resilience indices and restore all of the loads that have been affected by the faults. The bald eagle search algorithm (BESA) is used to identify the appropriate allocation of SPV-DG and solve the objective function within the system. The results of our tests show that our proposed technique has the capacity to enhancement the resilience and successfully restore all damaged loads in a distribution system.

The main aim of this paper is to design a local controller for DC/DC converter in a battery energy storage system (BESS) and a controller based on a virtual synchronous generator (VSG) for photovoltaic (PV) converter connected to AC grid. In the DC/DC converter design, the state feedback method is used so that the voltage and current control loops are combined, leading to higher flexibility and improved damping. This flexibility, which maintains the state of charge (SOC) of BESS, is supported by the design of virtual resistance and virtual capacitor statically and dynamically. In this design method, the BESS can be independently connected to each DC bus node and detect disturbances through local measurements. In addition, the improved Pre-Parallelism (PREP) method has been used to improve the transient performance caused by the disconnection and connection of additional loads, the existence of faults, and the inherent inertia difference in parallel operation between VSG and synchronous generator (SG). In this method, the problem of phase jump caused by transient disturbances is solved by considering a cosine function in VSG design. Also, to solve the problem of inertia difference between the units, a small signal model has been presented, in which, by considering the capacity ratio of the units on the AC side, the necessary inertia for VSG can be included in the design. The proposed method is simulated by considering different scenarios in MATLAB software, so the results demonstrate the superiority of the proposed controller compared to other existing methods.

This work proposes a cascaded five-level resonant boost “DC-DC converter with variable frequency control that is aimed at achieving a wide load and input voltage variation, remarkable efficiency, and zero voltage switching (ZVS) to meet the strict demands of industry applications. The design consideration of this converter was also discussed in detail to confirm the achievement of each feature of the multilevel and LLC resonant converter (RC) combination. The selection of the voltage gain was set to achieve an output voltage that is 5 times the input voltage; the operation frequency range” was selected within the range of $67kHz135kHz$ to ensure achievement of ZVS and the ability to work close to resonant frequency to achieve the estimated efficiency. Furthermore, a full-bridge LLC resonant frequency with the same assumptions and parameters was simulated using MATLAB/Simulink features to provide clear comparison and vision of the resonant behavior with the proposed topology. It was noticed that the LLC circuit with a five-level inverter differs from its typical behavior with full-bridge inverters; the switching frequency is always lower than the resonance frequency regardless of the input and load conditions. Also, the adopted variable frequency control proved its fast dynamic response to any changes in the input and load conditions to maintain $1000V$ output, and $5kW$ with 96.7% efficiency at full load conditions. Experiments were performed to validate the theoretical and simulation results using a laboratory prototype of the proposed converter; selected results were presented in this article to demonstrate the efficiency and viability of the proposed converter.

Fault detection and classification are fundamental requirements of multilevel inverters (MLIs) to ensure constant operation and improved reliability. Nowadays, the machine learning (ML) technique is utilized for fault diagnosis in MLIs due to its inherent features such as high accuracy, reduced computation time, and complexity. However, the rich availability of parts and classifiers in ML techniques demands a tedious investigation of every combination to design an optimal fault classifier. To overcome this problem, a combined optimiser is proposed to automate the ML-based classifier design, which involves selecting optimal features and classifier. Mean, total harmonic distortion, root mean square, and different harmonic orders (upto 12th order) of the output voltage of MLI are considered as features and four different ML techniques like K-nearest neighborhood, decision tree, Naive Bayesian classifier, and support vector machines are considered. Ant Colony Optimization (ACO) is used to formulate a combinatorial optimization routine to maximize classification accuracy by optimal selection of features and classifier. The proposed technique is used to design a fault classifier for two different MLIs, such as Cascaded H-bridge MLI (CHBMLI) and Packed U cell inverter (PUC), during the fault conditions (open circuit and short circuit) to check the feasibility. Simulation results illustrate classification accuracy of 97.84% and 98.61% for CHBMLI and PUC, respectively. Experimental validation of the designed classifier on the inverter prototype is also carried out and illustrates 95.56% and 94.28% classification accuracy for CHBMLI and PUC, respectively.

The rapidly increasing demand for electrical power and difficulties in providing the same using traditional power generating sources provide a motivation to integrate distributed generator (DG) in the radial distribution network. In this work, the optimal arrangement of DG using genetic algorithm (GA) is obtained based on fixed penetration level (PL). The state of charge (SoC) value for each hour before placing the battery based on the demand was estimated, then battery will be placed in the optimal location of the fixed capacity. This will help in reducing the power loss and simultaneous improvement of voltage profile of the network. Furthermore, electric vehicle (EV) is incorporated in the system. In the presence of EV, the loads are variable due to the charging and discharging of batteries in the EV. The variation of network power losses in the presence of EVs along with DGs and battery have been investigated on standard 33 and 69 bus systems for 2 different topologies and 2 different loading scenarios.

A high gain nonisolated DC-DC converter using a single power semiconductor switch is proposed in this article. The operation of the proposed converter is explained under continuous conduction mode (CCM), discontinuous conduction mode (DCM), and boundary conduction mode (BCM). The mathematical expressions for steady-state voltage gain, voltage stress, and current stress of diodes and switch are provided. Also, the design of inductors and capacitors in the CCM mode is explained with appropriate mathematical equations. The proposed topology is tested with a 200 W prototype at 50 kHz and a 60% duty cycle. The dynamic behavior of the proposed converter is examined by changing the duty cycle value and also load values. The proposed converter is verified with experimental results to prove the effectiveness of its operation. The proposed converter provides higher steady-state voltage gain as compared with recently developed topologies. The efficiency and power density of the proposed converter is 90% and 1.16 kW/L, respectively.

The electric power distribution plays a crucial part in the power systems to maintain the power quality and stability of the system for loss mitigation and voltage regulation. The reactive power compensators like a capacitor, DG, and DSTATCOM play a vital role in power quality and stability improvement by providing proper reactive power in the distribution system. This article provides a critical review and comparative analysis of various compensating devices allocation to achieve power loss mitigation and voltage regulation in the radial distribution system (RDS). The Bat Algorithm (BA) and Voltage Stability Index (VSI) are utilized for determining the optimal size and site of the compensating devices in the RDS. Further, the application of Blockchain technology is utilized for voltage regulation of energy trading RDS. To show the effectiveness of the present study, an IEEE 33 test system is considered. All three compensators are implemented in IEEE 33 test system and compared with existing approaches. The present study is very much helpful to the Distribution Network Operators for selecting the suitable compensator in real-time applications.

In recent years, smart consumers along with Distributed Generation (DGs) (Photovoltaic (PV) and wind) and Electric Vehicles (EV) are considered as prosumers. The prosumers trade the available excess power to the consumers for minimizing their electricity cost. Each appliance in the smart home can be scheduled by using Demand Response (DR) implementation based on the Real-Time Pricing (RTP). The implementation of peer-to-peer (P2P) energy trading in the smart home further minimizes the electricity cost of the consumer due to the energy trading from prosumers instead of the grid. This article deals with the impact of DR-based P2P energy trading among prosumers and consumers. In this work, two stages of scheduling are proposed to minimize the electricity cost of the consumers. The first stage represents the scheduling of each appliance in a smart home based on the RTP using the Binary Particle Swarm Optimization (BPSO) algorithm. The second stage represents the P2P energy trading among prosumers and consumers based on the DR implementation. The simulation results are proved that the reduction in electricity cost is achieved by implementing energy trading in the smart home. Also, the burden on the utility during the peak hour is reduced by implementing DR-based P2P energy trading.

Renewable energy (RE) is the key element of sustainable, environmentally friendly, and cost-effective electricity generation. An official report by International Energy Agency (IEA) states that the demand on fossil fuel usage to generate electricity has started to decrease since year 2019, along with the rise of RE usage to supply global energy demands. Researches on RE technologies are continuously growing in order to enhance the performance of RE generation, especially in term of energy conversion efficiency. The aim of this review paper is to understand and study further the current RE technologies such as solar energy, hydro energy, wind energy, bioenergy, geothermal energy, and hydrogen energy. Several hybrid RE technologies have been also studied and compared, to improve the overall performance of RE in generating electricity. Lastly, suggestions are provided for the purpose to solve and overcome the challenges and limitations of RE technologies in terms of economy, technical, and energy conversion efficiency.

Renewable-based sources can be interconnected through power electronic converters and connected with local loads and energy storage devices to form a microgrid. Nowadays, DC microgrids are gaining more popularity due to their higher efficiency and reliability as compared to AC microgrid systems. The DC Microgrid has power electronics converters between the DC loads and renewable-based energy sources. The power converters controlled with an efficient control algorithm for maintaining stable DC bus voltage in DC microgrids under various operating modes is a challenging task for researchers. With an aim to address the above-mentioned issues, this study focuses on the DC link voltage enhancement of a DC Microgrid system consisting of PV, DFIG-based wind energy conversion system (WECS), and battery Energy Storage System (ESS). To elevate PV output voltage and minimize the oscillations in DC link voltage, a high-gain Luo converter with Cascaded Fuzzy Logic Controller (CFLC) is proposed. Droop control with virtual inertia and damping control is proposed for DFIG-based WECS to provide inertia support. Artificial Neural Network (ANN) based droop control is utilised to regulate the ESS’s State of Charge (SOC). The effectiveness of the proposed converter and its control algorithms for maintaining stable DC bus link voltage has been analysed using MATLAB/Simulink and experimentally validated using a prototype model and FPGA Spartan 6E controllers.

Welding process is found to be a predominant procedure in most of the processing industries, especially in the automobile sector for maintenance operation and fabrication. Ultrasonic Polymer Welding (USW) is used for the joining process because of its flexibility and short needed welding time. In this article, two different polymer materials PC and ABS are blended in the ratio of 60:40 and molded into a sheet. Furthermore, molded PC/ABS sheets are joined using USW with different processing parameter settings. Three major influencing process parameters like pressure (P), amplitude (A) and weld time (Tw) are considered and other processing parameters are kept at constant. The experiment is carried out for 26 welded samples and from the obtained results it is noticed that the above-mentioned process parameters directly influence the tensile strength of welded joints. Additionally, the ultrasonically welded samples tensile strength is analyzed with the help of Artificial Neural Network technique (ANN) and Adaptive Neuro-Fuzzy Inference System method (ANFIS). From the simulation results, an optimized ANFIS model provides the superior result as compared to ANN. Moreover, Scanning Electron Microscope analysis is carried out to visualize the weld interface between the joint. Also, Finite Element Modeling (ANSYS) is performed to understand the heat dissipation during the welding process.

Electronic waste is an indirect and unimaginable waste which make adverse effects on the human, animals and environment by polluting the natural resources like air, soil and water. Accumulation and contamination of e-waste for a longer period may harshly affect the environmental resources. India and China are the largest consumers of electronic gadgets at the same time they are responsible for an increase in the waste electrical and electronic equipment. Therefore, this review article mainly focuses on the detailed explanation of the e-waste management system includes the recycling process and its effects in India. The uniqueness of this review article lies in the discussion of legal instruments and awareness programs in India at various periods of time. Also, it provides sufficient knowledge to the readers in various aspects of increasing e-waste and its controlling methods. As a result, it gives adequate information for reducing the utilization of e-product in consumer-side and control measures on the manufacturer-side. In addition to that, it will be helpful to the policymakers who are involving in framing the future policy of e-waste in India.

Increase in utilization of electric vehicles and penetration of Renewable Energy Sources (RES) creates a greater impact on traditional power systems. Intermittent output from renewable energy resources and uneven usage pattern of electric vehicles creates a greater impact on the economic operation of power system. In this article, a stochastic Dynamic Economic Dispatch (DED) problem incorporating Commercial Electric Vehicles (CEVs) and intermittent renewable energy resources is addressed. The charging pattern of CEVs as well as intermittency of solar and wind resources creates a significant impact on power system peak demand. The behaviour of these CEVs and RES in various seasons is considered and the same is tested on a real time south Indian practical test system. An attempt is made to improve the swarm-based Moth Flame Optimization (MFO), named as IMFO is developed and utilized for solving the considered problem. The efficacy of the said method is tested and necessary validations are carried out in this article.

Islanding detection and prevention are involved in tandem with the rise of large- and small-scale distribution grids. To detect islanded buses, either the voltage or the frequency variation has been considered in the literature. A modified passive islanding detection strategy that coordinates the V-F (voltage–frequency) index was developed to reduce the non-detection zones (NDZs), and an islanding operation is proposed in this article. Voltage and frequency were measured at each bus to check the violation limits by implementing the proposed strategy. The power mismatch was alleviated in the identified islands by installing a battery and a diesel generator, which prevented islanding events. The proposed strategy was studied on the three distinct IEEE radial bus distribution systems, namely, 33-, 69-, and 118-bus systems. The results obtained in the above-mentioned IEEE bus systems were promising when the proposed strategy was implemented. The results of the proposed strategy were compared with those of methods developed in the recent literature. As a result, the detection time and number of islanded buses are reduced.

Increasing the proportion of renewable energy generations in remote island power systems is becoming essential for realizing decarbonized society. However, since inverter-connected renewable energies have different generation characteristics from conventional generators, the massive penetration can adversely affect system stability. In particular, fault events in such weak remote systems can cause fast voltage collapse, and there is a need to assess dynamic voltage stability. This study attempts dynamic voltage stability assessment using the critical boundary index (CBI) and investigates the impact of the virtual synchronous generator (VSG) on dynamic voltage stability. A remote island power system and VSG are modeled, and time-domain simulations are conducted with case studies of fault events. The simulation results show the potential of CBI to use for dynamic voltage stability assessment. Furthermore, the VSG can provide suitable power output during fault events and improve dynamic voltage stability.

In this article, an efficient long-term novel scheduling technique is proposed for allocating capacitors in a combined system involving distributed generation (DG) along with radial distribution systems (RDS). We introduce a unique multi-objective function that focuses on the reduction of power loss with the maximization of voltage stability index (VSI) subjected to constraints of equality and inequality systems. Loss sensitivity factor and VSI together are involved in pre-identifying the locations of capacitors and DG. Determination of the optimal size of capacitor and DG is performed by utilizing the Bat algorithm (BA) for all the loads in RDS. The conventional approach considers the medium load of (1.0) condition generally, but the proposed method changes the feeder loads linearly, ranging from light load (0.5) to peak load (1.6) with the value of step size as 1%. BA determines the optimal size of the capacitor and DG for each step load. The curve fitting technique is used for deducing the generalized equation of capacitor size and DG for all conditions of the load with the various loading condition sized by distributed network operators (DNOs). Further, various load models such as industrial, residential, and commercial loads have been considered to show the efficiency of the present approach. Validation of results is performed in different scenarios on a 69-bus test system and on a standard IEEE 33-bus system. The results exhibit improved accuracy with less power loss value, superior bus voltage, and stability of system voltage with a higher rate of convergence.

The Frequency Response Analysis approach (FRA) is useful in the fault diagnosis of transformers. However, its usefulness in diagnosing any potential sources of Partial Discharge (PD) in transformers has not been thoroughly investigated so far. In this work, the use of Impulse voltage-based FRA (IFRA) in diagnosing inter-turn shorts and potential sources of PD were investigated on a 315 kVA, 11 kV/433 V transformer. Inter-turn shorts and PD sources were emulated and the usefulness of IFRA in their diagnosis was investigated while using switching impulse voltage at different magnitude levels as the test signals. For emulating the inter-turn shorts and the PDs, special tappings were provided on one of the 11 kV windings through the low capacitance bushings. Low voltage impulse was successful in diagnosing the inter-turn shorts, but unsuccessful in identifying the sources of PDs. During the test condition, the test voltage was adjusted with the presence of artificially created PD sources. The frequency response of the transformer before and after the inception of PD was observed and analyzed in this article. The FRA results demonstrated that the switching impulse voltage based IFRA approach at moderate voltages could be useful in diagnosing the presence of the potential sources of PDs.

The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads.