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Power quality is a pressing concern and of the utmost importance for advanced and high-tech equipment in particular, whose performance relies heavily on the supply’s quality. Power quality issues like voltage sags/swells, harmonics, interruptions, etc. are defined as any deviations in current, voltage, or frequency that result in end-use equipment damage or failure. Sensitive loads like medical equipment in hospitals and health clinics, schools, prisons, etc. malfunction for the outages and interruptions, thereby causing substantial economic losses. For enhancing power quality, custom power devices (CPDs) are recommended, among which the Dynamic Voltage Restorer (DVR) is considered as the best and cost-effective solution. DVR is a power electronic-based solution to mitigate and compensate voltage sags. This paper provides a thorough discussion and comprehensive review of DVR topologies based on operations, power converters, control methods, and applications. The review compares the state-of-the-art in works of literature, and comparative study on power quality issues, the DVR principle along with its operation modes, the DVR components, the DVR topologies based on energy storage, the DVR topologies based on single-/three-phase power converters, and the DVR topologies based on control units that have different control processing stages. Furthermore, modified and improved configurations of the DVR, as well as its integration with distributed generations, are described. This work serves as a comprehensive and useful reference for those who have an interest in researching DVRs.

Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer.

The bearingless concept is a plausible alternative to the magnetic bearing drives. It provides numerous advantages like minimal maintenance, low cost, compactness and no requirement of high-performance power amplifiers. Controlling the rotor position and its displacements under parameter variations during acceleration and deceleration phases was not effective with the use of conventional controllers like proportional–integral–derivative (PID) and fuzzy-type controllers. Hence, to get the robust and stable operation of a bearingless switched reluctance motor (BSRM), a new robust dynamic sliding mode controller has been proposed in this paper, along with a sensorless operation using a sliding ode observer. The rotor displacement tracking error functions and speed tracking error functions are used in the designing of both proposed methods of the sliding mode switching functions. To get a healthy and stable operation of the BSRM, the proposed controller’s tasks are divided into three steps. As a first step, the displaced rotor in any one of the four quadrants in the air gap has to pull back to the centre position successfully. The second step is to run the motor at a rated speed by exciting torque phase currents, and finally, the third step is to maintain the stable and robust operation of the BSRM even under the application of different loads and changes of the motor parameters. Simulation studies were conducted and analysed under different testing conditions. The suspension forces, rotor displacements, are robust and stable, and the rotor is pulled back quickly to the centre position due to the proposed controller’s actions. The improved performance characteristics of the dynamic sliding mode controller (DSMC)-based sliding mode observer (SMO) was compared with the conventional sliding mode controller (SMC)-based SMO.

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

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.

Photovoltaic (PV) water pumping systems are becoming popular these days. In PV water pumping, the role of the converter is most important, especially in the renewable energy-based PV systems case. This study focuses on one such application. In this proposed work, direct current (DC) based intermediate DC-DC power converter, i.e., a modified LUO (M-LUO) converter is used to extricate the availability of power in the high range from the PV array. The M-LUO converter is controlled efficiently by utilizing the Grey Wolf Optimizer (GWO)-based maximum power point tracking algorithm, which aids the smooth starting of a brushless DC (BLDC) motor. The voltage source inverter’s (VSI) fundamental switching frequency is achieved in the BLDC motor by electronic commutation. Hence, the occurrence of VSI losses due to a high switching frequency is eliminated. The GWO optimized algorithm is compared with the perturb and observe (P&O) and fuzzy logic based maximum power point tracking (MPPT) algorithms. However, by sensing the position of the rotor and comparing the reference speed with the actual speed, the speed of the BLDC motor is controlled by the proportional-integral (PI) controller. The recent advancement in motor drives based on distributed sources generates more demand for highly efficient permanent magnet (PM) motor drives, and this was the beginning of interest in BLDC motors. Thus, in this paper, the design of a high-gain boost converter optimized by a GWO algorithm is proposed to drive the BLDC-based pumping motor. The proposed work is simulated in MATLAB-SIMULINK, and the experimental results are verified using the dsPIC30F2010 controller.

The Complementary Metal-Oxide Semiconductor (CMOS) technology has evolved to a great extent and is being used for different applications like environmental, biomedical, radiofrequency and switching, etc. Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) based biosensors are used for detecting various enzymes, molecules, pathogens and antigens efficiently with a less time-consuming process involved in comparison to other options. Early-stage detection of disease is easily possible using Field-Effect Transistor (FET) based biosensors. In this paper, a steep subthreshold heavily doped n+ pocket asymmetrical junctionless MOSFET is designed for biomedical applications by introducing a nanogap cavity region at the gate-oxide interface. The nanogap cavity region is introduced in such a manner that it is sensitive to variation in biomolecules present in the cavity region. The analysis is based on dielectric modulation or changes due to variation in the bio-molecules present in the environment or the human body. The analysis of proposed asymmetrical junctionless MOSFET with nanogap cavity region is carried out with different dielectric materials and variations in cavity length and height inside the gate–oxide interface. Further, this device also showed significant variation for changes in different introduced charged particles or region materials, as simulated through a 2D visual Technology Computer-Aided Design (TCAD) device simulator.

Infrared Thermography has been used as a tool for predictive and preventive maintenance of Photovoltaic panels. International Electrotechnical Commission provides some guidelines for using thermography to detect defects in Photovoltaic panels. However, the proposed guidelines focus only on the location of the hot spot than diagnosing the types of faults. The long-term reliability and efficiency of panels can be affected by progressive defects such as discolouring and delamination. This paper proposed the new Thermal Pixel Counting algorithm to detect the above faults based on three thermal profile index values. The real-time experimental testing was carried out using FLIR T420bx® thermal imager and results have been provided to validate the proposed method. In this work, the fuzzy rule-based classification system is proposed to automate the classification process. Fuzzy reasoning method based on a single winner rule fuzzy classifier is designed with modified rule weights by particular grade. The performance of the proposed classifier is compared with the conventional fuzzy classifier and neural network model.

The friction welding of tube to tube plate using an external tool (FWTPET) is widely deployed in several industrial applications, such as aerospace, automotive, and power plants. Moreover, for achieving a better tensile strength and hardness in the weld zone, the friction stir processing (FSP) technique was incorporated into the FWTPET process for joining aluminum alloys (AA6063 tube, AA6061 tube plate). Furthermore, it has to be noted that FWTPET was applied for joining the AA6063 tube to the AA6061 tube plate, and FSP was deployed for reinforcing the weld zone with carbon nanotube (CNT) and silicon nitride (Si3N4) particles, thereby attaining the desirable mechanical properties. Subsequently, the Taguchi L25 orthogonal array was used for identifying the most influential input and output FWTPET + FSP process parameters. Furthermore, particle swarm optimization (PSO) and the firefly algorithm (FFA) were deployed for determining the optimized input and output FWTPET + FSP process parameters. The input process parameters include CNT, Si3N4, rotational tool speed, and depth. Furthermore, the tensile strength of the welded joint was considered as the output process parameter. The process parameters predicted by PSO and FFA were compared with the experimental values. It was witnessed that deviation between the predicted and experimental values was minimal. Moreover, it was found that FFA provided a superior tensile strength prediction than PSO.

This paper proposes an effective management system for stand-alone solar photovoltaic (PV) using real-time data with Hybrid Energy Storage System (HESS). The abrupt movement of fleeting clouds often gives rise to PV power output fluctuations which in turn affect the power quality and system stability due to scattered solar radiation reception. These variations can limit through a ramp-limit controller and employing a DC link controller to maintain the stable DC link voltage. The battery is used in the system for continuous power application and the sudden variations in charging and discharging of battery power can create stress on the battery. These sudden changes in a battery will be removed by the super-capacitor (SC) unit and achieves a fast DC link voltage regulation. Hence, the high energy and power density devices such as battery and SC units will deliver more stable power into the system. The control scheme is tested in Matlab/Simulink and validated by Real-Time Hardware-in-Loop (HIL) simulator using periodic one-minute data for one year from the solar PV power plant from real-time.

The quasi-impedance source inverters/quasi-Z source inverters (Q-ZSIs) have shown improvement to overwhelmed shortcomings of regular voltage-source inverters (VSIs) and current-source inverters (CSIs) in terms of efficiency and buck-boost type operations. The Q-ZSIs encapsulated several significant merits against conventional ZSIs, i.e., realized buck/boost, inversion and power conditioning in a single power stage with improved reliability. The conventional inverters have two major problems; voltage harmonics and boosting capability, which make it impossible to prefer for renewable generation and general-purpose applications such as drive acceleration. This work has proposed a Q-ZSI with five-level six switches coupled inverter. The proposed Q-ZSI has the merits of operation, reduced passive components, higher voltage boosting capability and high efficiency. The modified space vector pulse width modulation (PWM) developed to achieve the desired control on the impedance network and inverter switching states. The proposed PWM integrates the boosting and regular inverter switching state within one sampling period. The PWM has merits such as reduction of coupled inductor size, total harmonic reduction with enhancing of the fundamental voltage profile. In comparison with other multilevel inverters (MLI), it utilizes only half of the power switch and a lower modulation index to attain higher voltage gain. The proposed inverter dealt with photovoltaic (PV) system for the stand-alone load. The proposed boost inverter topology, operating performance and control algorithm is theoretically investigated and validated through MATLAB/Simulink software and experimental upshots. The proposed topology is an attractive solution for the stand-alone and grid-connected system.

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.

In the above-named work, T. Deepa should have been listed as the second co-author of the article with the affiliation of (1): School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, India. The author's biography is also provided within this correction. Additionally, the correct zip code of affiliation (1) should be 600127, and the correct statement on financial support acknowledgement should be as follows: "This work was funded by the Renewable Energy Laboratory, Department of Communications and Networks Engineering, Prince Sultan University, Riyadh, Saudi Arabia." It is necessary to mention the nature of funding provided by Prince Sultan University and to note the correction in the spelling of the university in the same statement in the published manuscript.

This paper considered a six-phase (asymmetrical) induction motor, kept 30° phase displacement between two set of three-phase open-end stator windings configuration. The drive system consists of four classical three-phase voltage inverters (VSIs) and all four dc sources are deliberately kept isolated. Therefore, zero-sequence/homopolar current components cannot flow. The original and effective power sharing algorithm is proposed in this paper with three variables (degree of freedom) based on synchronous field oriented control (FOC). A standard three-level space vector pulse width modulation (SVPWM) by nearest three vectors (NTVs) approach is adopted to regulate each couple of VSIs. The proposed power sharing algorithm is verified by complete numerical simulation modeling (Matlab/Simulink-PLECS software) of whole ac drive system by observing the dynamic behaviors in different designed condition. Set of results are provided in this paper, which confirms a good agreement with theoretical development.

For high-voltage (HV) applications, the designers mostly prefer the classical DC–DC boost converter. However, it lacks due to the limitation of the output voltage by the gain transfer ratio, decreased efficiency and its requirement of two sensors for feedback signals, which creates complex control scheme with increased overall cost. Furthermore, the output voltage and efficiency are reduced due to the self-parasitic behavior of power circuit components. To overcome these drawbacks, this manuscript provides, the theoretical development and hardware implementation of DC–DC step-up (boost) power converter circuit for obtaining extra output-voltage high-performance. The proposed circuit substantially improves the high output-voltage by voltage-lift technology with a closed loop proportional–integral controller. This complete numerical model of the converter circuit including closed loop P-I controller is developed in simulation (Matlab/Simulink) software and the hardware prototype model is implemented with digital signal processor (DSP) TMS320F2812. A detailed performance analysis was carried out under both line and load regulation conditions. Numerical simulation and its verification results provided in this paper, prove the good agreement of the circuit with theoretical background.

Classical DC-DC converters used in high voltage direct current (HVDC) power transmission systems, lack in terms of efficiency, reduced transfer gain and increased cost with sensor (voltage/current) numbers. Besides, the internal self-parasitic behavior of the power components reduces the output voltage and efficiency of classical HV converters. This paper deals with extra high-voltage (EHV) dc-dc boost converter by the application of voltage-lift technique to overcome the aforementioned deficiencies. The control strategy is based on classical proportional-integral (P-I) and fuzzy logic closed-loop controller to get high and stable output voltage. Complete hardware prototype of EHV is implemented and experimental tasks are carried out with digital signal processor (DSP) TMS320F2812. The control algorithms P-I, fuzzy logic and the pulse-width modulation (PWM) signals for N-channel MOSFET device are performed by the DSP. The experimental results provided show good conformity with developed hypothetical predictions. Additionally, the presented study confirms that the fuzzy logic controller provides better performance than classical P-I controller under different perturbation conditions.

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.

Voltage collapse in power systems is still considered the greatest threat, especially for the transmission system. This is directly related to the quality of the power, which is characterized by the loss of a stable operating point and the deterioration of voltage levels in the electrical center of the region exposed to voltage collapse. Numerous solution methods have been investigated for this undesirable degradation. This paper focuses on the steady state/dynamic stability subcategory and techniques that can be used to analyze and control the dynamic stability of a power system, especially following a minor disturbance. In particular, the failure of one generator among the network with a large number of synchronous generators will affect other synchronous generators. This will become a major problem and it will be difficult to find or resolve the fault in the network due to there being too many variables, consequently affecting the stability of the entire system. Since the solution of large matrices can be completed more easily in this complex system using the Householder method, which is a small signal stability analysis method that is suggested in the thesis, the detection of error and troubleshooting can be performed in a shorter period of time. In this paper, examples of different rotor angle deviations of synchronous generators were made by simulating rotor angle stability deviations up to five degrees, allowing the system to operate stably, and concluding that the system remains constant.

Locating electric vehicle charge stations has always been an important problem for electric distributers. Many basic and complex solutions have been provided by algorithms and methods to solve this problem in real and assumed grids. However, the data, which has been used in those algorithms, are not consistent with the diversity of locations, thus, do not meet the expected results. Grid locations are the most important aspects of this issue in the eyes of designers, investors, and the general public. Locating charge stations must be determined by plans which have influenced majority in the society. In some countries, power quality has been increased by storages, which are used in vehicle-to-grid (V2G) and similar operations. In this paper, all of the variables for locating charging stations are explained according to Ankara metropolitan. During the implemented analysis and literature reviews, an algorithm, based on location and grid priorities and infrastructures, are 154 kV and 33 kV, have been designed. Genetic algorithms have been used to demonstrate this method even though other algorithms can also be adopted to meet the priority.

Microgrids are distribution networks consisting of distributed energy sources such as photovoltaic and wind turbines, that have traditionally been one of the most popular sources of energy. Furthermore, microgrids consist of energy storage systems and loads (e.g., industrial and residential) that may operate in grid-connected mode or islanded mode. While microgrids are an efficient source in terms of inexpensive, clean and renewable energy for distributed renewable energy sources that are connected to the existing grid, these renewable energy sources also cause many difficulties to the microgrid due to their characteristics. These difficulties mainly include voltage collapses, voltage and frequency fluctuations and phase difference faults in both islanded mode and in the grid-connected mode operations. Stability of the microgrid structure is necessary for providing transient stability using intelligent optimization methods to eliminate the abovementioned difficulties that affect power quality. This paper presents optimization and control techniques that can be used to provide transient stability in the islanded or grid-connected mode operations of a microgrid comprising renewable energy sources. The results obtained from these techniques were compared, analyzing studies in the literature and finding the advantages and disadvantages of the various methods presented. Thus, a comprehensive review of research on microgrid stability is presented to identify and guide future studies.

This article aims to enhance the output voltage magnitude of fuel cells (FCs), since the actual generation is low. The traditional technique is too complicated and has a cascaded or parallel connection solution to achieve high voltage for multiple loads in vehicles. In this case, electronic power converters are a viable solution with compact size and cost. Hence, double or multiple output DC−DC converters with high voltage step up are required to feed multiple high voltage loads at the same time. In this article, novel double stage double output (DSDO) DC−DC converters are formulated to feed multiple high voltage loads of FC vehicular system. Four DSDO DC−DC converters called DSDO L−L, DSDO L-2L, DSDO L-2LC, and DSDO L-2LC are developed in this research work and all the converters are derived based on the arrangement of different reactive networks. The primary power circuitry, conceptual operation, and output voltage gain derivation are given in detail with valid proof. The proposed converters are compared with possible parallel combinations of conventional converters and recently available configuration. Comprehensive numerical simulation and experimental prototype results show that our theoretical predictions are valid and that the configuration is applicable for real time application in FC technologies for ‘more-electric vehicles’.

The control of tension in processing lines for metal strips tackles several problems. The process of achieving high tension driven by a multi-motor drive system, where the motors are mechanically coupled by a strip, is affected by the maximal torque of each drive, by friction between the strip and the surface of the tension roll, and by the wrap angle. The friction itself and the wrap angle are described by the eµα factor, which can be also calculated as the ratio of tensions in the strip in the previous section and subsequent section of the multi-motor drive. In this paper, an algorithm for the proper distribution of tensions in the strip for a multi-motor drive system of a continuous processing line is revealed. The algorithm ensures the tension distribution among particular drives of the tension leveler while respecting the physical limits of the drives and also preserving the desired conditions of a constant ratio between the input and output tensions for all drives in the leveler. The algorithm also prevents overloading of the drives. Finally, the algorithm was implemented in a control system of a strip processing line, and the obtained results correspond with the simulations. This, in turn, confirms the correctness of the algorithm design.

This work presents an economic analysis of a hybrid renewable energy source (HRES) integrated with an energy storage system (ESS) using batteries with a new proposed strategy. Here, the HRES system comprises wind turbines (WT) and a photovoltaic (PV) system. The hybrid WT, PV and energy storage system with battery offer several benefits, in particular, high wind generation utilization rate, and optimal generation for meeting supply-demand gaps. The real recorded data of various parameters of a 22 KV hybrid ‘Regen’ feeder of 110/22 KV Vagarai Substation of TANTRANSCO in Palani of Tamilnadu in India was gathered, studied for the entire year of 2018, and utilized in this paper. The proposed strategy is the hybridization of two algorithms called Radial Basis Function Neural Network (RBFNN) and Oppositional Elephant Herding Optimization (OEHO) named the RBFNOEHO technique. With the help of RBFNN, the continuous load demand required for the HRES and be tracked. OEHO is used to optimize a perfect combination of HRES with the predicted load demand. The aim of the proposed hybrid RBFNOEHO is to study the cost comparison of the HRES system with the existing conventional base method, energy storage method (ESS) with batteries and with HOMER. The proposed Hybrid RBFNOEHO technique is evaluated by comparing it with the other techniques; it is found that the proposed method yields a more optimal solution than the other techniques.

The world has witnessed a rapid transformation in the field of electrical generation, transmission and distribution. We have been constantly developing and upgrading our technology to make the system more economically efficient. Currently, the industry faces an acute shortage of energy resources due to overconsumption by industries worldwide. This has compelled experts to look for alternatives to fossil fuels and other conventional sources of energy to produce energy in a more sustainable manner. The microgrid concept has gained popularity over the years and has become a common sight all over the world because of the ability of a microgrid to provide power to a localized section without being dependent on conventional resources. This paper focuses on development of such an AC hybrid microgrid, which receives power from distributed energy resources (DERs) such as a PV array alongside a battery storage system, and also uses an emergency diesel generator system and an online uninterruptible power supply (UPS) system to provide power to predefined loads under different conditions. This paper also addresses on the power flow to the loads under two main modes of operation—on grid and off grid—and investigates the microgrid in different states and sub-states. The final objective is to design an efficient microgrid model such that it can sustain the multiple loads simultaneously under all operating conditions.

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260−280° and wind from 0−4 m/s were present in sector 170−180° of the Tirumala region in India.

A permanent magnet assisted synchronous reluctance generator (PMA-SynRG) and an induction generator (IG) were compared for portable generator applications. PMA-SynRG with two rotor configurations, namely rotors with ferrite magnet and NdFeB, were designed. Furthermore, a design strategy for both PMA-SynRG and IG is presented with their geometrical dimensions. The machine was designed and results were analyzed using finite element analysis. Results such as flux density, open circuit and full load voltages, torque in generating mode, weight comparison and detailed cost analysis were investigated. In addition, thermal analysis for various ambient conditions (−40 °C, +30 °C, +65 °C) was evaluated for both PMA-SynRG and IG. Furthermore, acoustic versus frequency plot and acoustic pressure level were investigated for both the generators. Finally, the results confirmed that the machine with a higher power-to-weight ratio was the right choice for military applications.

In recent years, many applications have been developed for the integration of renewable energy sources (RES) into the grid in order to satisfy the demand requirement of a clean and reliable electricity generation. Increasing the number of RES creates uncertainty in load and power supply generation, which also presents an additional strain on the system. These uncertainties will affect the voltage and frequency variation, stability, protection, and safety issues at fault levels. RES present non-linear characteristics, which requires effective coordination control methods. This paper presents the stability issues and solutions associated with the integration of RES within the grid.

Solar Photovoltaic (PV) systems have been in use predominantly since the last decade. Inverter fed PV grid topologies are being used prominently to meet power requirements and to insert renewable forms of energy into power grids. At present, coping with growing electricity demands is a major challenge. This paper presents a detailed review of topological advancements in PV-Grid Tied Inverters along with the advantages, disadvantages and main features of each. The different types of inverters used in the literature in this context are presented. Reactive power is one of the ancillary services provided by PV. It is recommended that reactive power from the inverter to grid be injected for reactive power compensation in localized networks. This practice is being implemented in many countries, and researchers have been trying to find an optimal way of injecting reactive power into grids considering grid codes and requirements. Keeping in mind the importance of grid codes and standards, a review of grid integration, the popular configurations available in literature, Synchronization methods and standards is presented, citing the key features of each kind. For successful integration with a grid, coordination between the support devices used for reactive power compensation and their optimal reactive power capacity is important for stability in grid power. Hence, the most important and recommended intelligent algorithms for the optimization and proper coordination are peer reviewed and presented. Thus, an overview of Solar PV energy-fed inverters connected to the grid is presented in this paper, which can serve as a guide for researchers and policymakers.

Frequency stability is an important issue for the operation of islanded microgrids. Since the upstream grid does not support the islanded microgrids, the power control and frequency regulation encounter serious problems. By increasing the penetration of the renewable energy sources in microgrids, optimizing the parameters of the load frequency controller plays a great role in frequency stability, which is currently being investigated by researchers. The status of loads and generation sources are received by the control center of a microgrid via a communication system and the control center can regulate the output power of renewable energy sources and/or power storage devices. An inherent delay in the communication system or other parts like sensors sampling rates may lead microgrids to have unstable operation states. Reducing the delay in the communication system, as one of the main delay origins, can play an important role in improving fluctuation mitigation, which on the other hand increases the cost of communication system operation. In addition, application of ultra-capacitor banks, as a virtual inertial tool, can be considered as an effective solution to damp frequency oscillations. However, when the ultra-capacitor size is increased, the virtual inertia also increases, which in turn increases the costs. Therefore, it is essential to use a suitable optimization algorithm to determine the optimum parameters. In this paper, the communication system delay and ultra-capacitor size along with the parameters of the secondary controller are obtained by using a Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm as well as by considering the costs. To cover frequency oscillations and the cost of microgrid operation, two fitness functions are defined. The frequency oscillations of the case study are investigated considering the stochastic behavior of the load and the output of the renewable energy sources.

PV generating sources are one of the most promising power generation systems in today’s power scenario. The inherent potential barrier that PV possesses with respect to irradiation and temperature is its nonlinear power output characteristics. An intelligent power tracking scheme, e.g., maximum power point tracking (MPPT), is mandatorily employed to increase the power delivery of a PV system. The MPPT schemes experiences severe setbacks when the PV is even shaded partially as PV exhibits multiple power peaks. Therefore, the search mechanism gets deceived and gets stuck with the local maxima. Hence, a rational search mechanism should be developed, which will find the global maxima for a partially shaded PV. The conventional techniques like fractional open circuit voltage (FOCV), hill climbing (HC) method, perturb and observe (P&O), etc., even in their modified versions, are not competent enough to track the global MPP (GMPP). Nature-inspired and bio-inspired MPPT techniques have been proposed by the researchers to optimize the power output of a PV system during partially shaded conditions (PSCs). This paper reviews, compares, and analyzes them. This article renders firsthand information to those in the field of research, who seek interest in the performance enhancement of PV system during inhomogeneous irradiation. Each algorithm has its own advantages and disadvantages in terms of convergence speed, coding complexity, hardware compatibility, stability, etc. Overall, the authors have presented the logic of each global search MPPT algorithms and its comparisons, and also have reviewed the performance enhancement of these techniques when these algorithms are hybridized.

Among the total energy consumption by utilities, pumping systems contribute 30%. It is evident that a tremendous energy saving potential is achievable by improving the energy efficiency and reducing faults in the pumping system. Thus, optimal operation of centrifugal pumps throughout the operating region is desired for improved energy efficiency and extended lifetime of the pumping system. The major harmful operations in centrifugal pumps include cavitation and water hammering. The pump faults are simulated in a real-time experimental setup and the operating point of the pump is estimated correspondingly. In this article, the experimental power quality and vibration measurements of cascade pumps during cavitation and water hammering is recorded for different operating conditions. The results are compared with the normal operating conditions of the pumping system for fault prediction and parameter estimation in a cascade water pumping system. Moreover, the Fast Fourier Transform (FFT) analysis comparison of normal and water hammering (faulty condition) highlights the frequency response of the pumping system. Also, the various power quality issues, i.e., voltage, current, total harmonic distortion, power factor, and active, reactive, and apparent power for a cascade multipump control is discussed in this article. The vibration, FFT, and various power quality measurements serve as input data for the classification of faulty pump operating condition in contrast with the normal operation of pumping system.

In this paper, a Flower Pollination Algorithm (FPA) has been proposed for relieving congestion in the deregulated power electricity industry. Congestion in the power market is one the contemplative challenges to be overcome in the era of deregulation. The primary cause of congestion is due to the loss of the transmission line, an increase in load, or loss of generator(s). Hence, managing congestion is one of the issues which have to be tackled in the present scenario. There are several techniques to relieve congestion. It is quite well-known that the thermal limits of transmission lines in a power system are fixed. One of the methods to abate congestion is to reschedule the real power of the generators. The purpose of the present work is to benefit the Independent System Operator (ISO) in reliving congestion. (1) In order to meet this objective effectively, a FPA algorithm has been proposed for relieving congestion and is simulated on a modified IEEE 30-bus system initially. (2) Congestion cost, compared with and without the application of FPA, is computed. (3) To validate its effectiveness, the obtained results are compared with recent power system optimization algorithms present in the literature. (4) Further, the work has been extended with the incorporation of a Pumped Hydro Storage Unit (PHSU). Here an economic analysis of congestion cost reduction employing FPA before and after the incorporation of PHSU is investigated applying FPA. In comparison with other evolutionary algorithms, the uniqueness of generating a new population is attained in FPA by the levy flight procedure. It is one of the latest evolved algorithms and is suited for different power system problem due to fewer clear-cut tuning parameters in contrast with other algorithms. (5) Furthermore, the effects of other network parameters, including system losses and voltage, has been computed. The result obtained is tested in terms of congestion mitigation with and without the incorporation of PHSU, in terms of novel objective improvement, and with and without applying recently evolving FPA for the above application. Thus the objective-wise and algorithmic-wise innovative concept has been presented. This proves effectiveness of the algorithm in terms of minimized cost convergence and other parameter including system losses and voltage before and after the incorporation of PHSU as compared with other recent trendsetting reported optimization techniques.

This paper proposes the modified, extended Kalman filter, neural network-based model reference adaptive system and the modified observer technique to estimate the speed of a five-phase induction motor for sensorless drive. The proposed method is generated to achieve reduced speed deviation and reduced torque ripple efficiently. In inclusion, the result of speed performance and torque ripple under parameter variations were analysed and compared with the conventional direct synthesis method. The speed estimation of a five-phase motor in the four methods is analysed using MATLAB Simulink platform, and the optimum method is recognized using time domain analysis. It is observed that speed error is minimized by 60% and torque ripple is reduced by 75% in the proposed method. The hardware setup is carried out for the optimized method identified.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems, in which system architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope with current Information and Communication Technologies (ICT). The distributed generation in a smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible, and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review of architectures for smart grids. An in depth analysis of layered and agent-based architectures based on the National Institute of Standards and Technology (NIST) conceptual model is presented. Also presented is a set of smart grid Reference Architectures dealing with cross domain technology.

This research work explains the practical realization of hybrid solar wind-based standalone power system with maximum power point tracker (MPPT) to produce electrical power in rural places (residential applications). The wind inspired Ant Colony Optimization (ACO)-based MPPT algorithm is employed for the purpose of fast and accurate tracking power from wind energy system. Fuzzy Logic Control (FLC) inverter controlling strategy is adopted in this presented work compared to classical proportional-integral (PI) control. Moreover, single Cuk converter is operated as impedance power adapter to execute MPPT functioning. Here, ACO-based MPPT has been implemented with no voltage and current extra circuit requirement compared to existing evolutionary algorithms single cuk converter is employed to improve conversion efficiency of converter by maximizing power stages. DC-link voltage can be regulated by placing Cuk converter Permanent Magnet Synchronous Generator (PMSG) linked rectifier and inverter. The proposed MPPT method is responsible for rapid battery charging and gives power dispersion of battery for hybrid PV-Wind system. ACO-based MPPT provides seven times faster convergence compared to the particle swarm optimization (PSO) algorithm for achievement of maximum power point (MPP) and tracking efficiency. Satisfactory practical results have been realized using the dSPACE (DS1104) platform that justify the superiority of proposed algorithms designed under various operating situations.

A hybrid microgrid-powered charging station reduces transmission losses with better power flow control in the modern power system. However, the uncoordinated charging of battery electric vehicles (BEVs) with the hybrid microgrid results in ineffective utilization of the renewable energy sources connected to the charging station. Furthermore, planned development of upcoming charging stations includes a multiport charging facility, which will cause overloading of the utility grid. The paper analyzes the following technical issues: (1) the energy management strategy and converter control of multiport BEV charging from a photovoltaic (PV) source and its effective utilization; (2) maintenance of the DC bus voltage irrespective of the utility grid overloading, which is caused by either local load or the meagerness of PV power through its energy storage unit (ESU). In addition, the charge controller provides closed loop charging through constant current and voltage, and this reduces the charging time. The aim of an energy management strategy is to minimize the usage of utility grid power and store PV power when the vehicle is not connected for charging. The proposed energy management strategy (EMS) was modeled and simulated using MATLAB/Simulink, and its different modes of operation were verified. A laboratory-scale experimental prototype was also developed, and the performance of the proposed charging station was investigated.

This paper proposes an intelligent battery management system (BMS) implementing two large Vanadium Redox Battery (VRB) flow batteries in a master-slave mode to provide grid-level energy storage for a wind-solar hybrid power system. The proposed BMS is formulated to effectively meet a predetermined power dispatch formulated based on forecasted wind and solar data while incorporating features like peak shaving and ramp rate limiting. It is compared to a single battery module operated system to showcase the advantages of the proposed intelligent dual battery module in terms of appreciable reduction in battery size and costs while exhibiting improved lifecycle performance. The battery size is optimized based on heuristic optimization algorithms and modelled in Matlab/Simulink environment. An intelligent fuzzy-based BMS is used to control the dual VRB model to ensure optimized power sharing between batteries. The simulations were carried out and an in-depth economic analysis conducted to analyze the costs and other financial metrics of the hybrid project. Results proved the advantages of the dual battery with the proposed BMS and fortify that the introduction of time-based tariffs and other incentives will further make investments in VRB highly attractive for renewable applications.

A PV-Simulator is a DC power source in which the current-voltage (I-V) characteristics of different PV arrays can be programmed. With a PV-simulator, the operation of the solar power conditioning systems can be validated at a laboratory level itself before actual field trials. In this work, design, operation and controls for a two-stage programmable PV-simulator required for the testing of solar power conditioning systems are presented. The proposed PV-simulator consists of a three-level T-type active front-end converter in the first stage and a buck-chopper-based DC-DC converter in the second stage. An active front-end rectifier using a three-level T-type IGBT-based converter is used at the input stage to help in operating the system at unity power factor. A DC-DC converter at the output stage of the simulator is regulated to obtain the I-V characteristics of the programmed PV-Array. Hardware-In-Loop simulations are carried out to validate the proposed system and the associated controls implemented in the controller. As a case study, this PV-simulator is programmed with electrical parameters of a selected PV-array and the characteristics obtained from the PV-simulator are compared with the actual PV-array characteristics. The dynamic response of the system for sudden changes in the load and sudden changes in irradiance values are studied.

The increase in the emission of greenhouse gases (GHG) is one of the most important problems in the world. Decreasing GHG emissions will be a big challenge in the future. The transportation sector uses a significant part of petroleum production in the world, and this leads to an increase in the emission of GHG. The result of this issue is that the population of the world befouls the environment by the transportation system automatically. Electric Vehicles (EV) have the potential to solve a big part of GHG emission and energy efficiency issues such as the stability and reliability of energy. Therefore, the EV and grid relation is limited to the Vehicle-to-Grid (V2G) or Grid-to-Vehicle (G2V) function. Consequently, the grid has temporary energy storage in EVs’ batteries and electricity in exchange for fossil energy in vehicles. The energy actors and their research teams have determined some targets for 2050; hence, they hope to decrease the world temperature by 6 °C, or at least by 2 °C in the normal condition. Fulfilment of these scenarios requires suitable grid infrastructure, but in most countries, the grid does not have a suitable background to apply in those scenarios. In this paper, some problems regarding energy scenarios, energy storage systems, grid infrastructure and communication systems in the supply and demand side of the grid are reviewed.

This paper presents different multi-carrier unipolar trapezoidal pulse width modulation strategies for a reduced switch asymmetrical multilevel inverter. The different strategies are phase disposition, alternative phase opposition and disposition, and carrier overlapping and variable frequency that involve triangular waves as carriers with a unipolar trapezoidal wave as a reference. The reduced switch, asymmetrical multilevel inverter operation was examined for generating the seven-level output voltage using Matlab/Simulink 2009b and the results were verified with a real-time laboratory-based experimental setup using a field-programmable gate array. Different parameter analyses, such as total harmonic distortion, fundamental root mean square voltage, and distortion factor, were analyzed with different modulation indices to investigate the performance of the selected topology. Unipolar trapezoidal pulse width modulation provides a higher root mean square voltage value.

This research work deals with the modeling and control of a hybrid photovoltaic (PV)-Wind micro-grid using Quasi Z-source inverter (QZsi). This inverter has major benefits as it provides better buck/boost characteristics, can regulate the phase angle output, has less harmonic contents, does not require the filter and has high power performance characteristics over the conventional inverter. A single ended primary inductance converter (SEPIC) module used as DC-DC switched power apparatus is employed for maximum power point tracking (MPPT) functions which provide high voltage gain throughout the process. Moreover, a modified power ratio variable step (MPRVS) based perturb & observe (P&O) method has been proposed, as part of the PV MPPT action, which forces the operating point close to the maximum power point (MPP). The proposed controller effectively correlates with the hybrid PV, Wind and battery system and provides integration of distributed generation (DG) with loads under varying operating conditions. The proposed standalone micro grid system is applicable specifically in rural places. The dSPACE real-time hardware platform has been employed to test the proposed micro grid system under varying wind speed, solar irradiation, load cutting and removing conditions etc. The experimental results based on a real-time digital platform, under dynamic conditions, justify the performance of a hybrid PV-Wind micro-grid with Quasi Z-Source inverter topology.

The AC and DC power system structures need to be modernized to meet consumer demands. DC microgrids are suitably admired due to their high efficiency, consistency, reliability, and load sharing performance, when interconnected to DC renewable and storage sources. The main control objective for any DC microgrid is providing proper load–power balancing based on the Distributed Generator (DG) sources. Due to the intermittent nature of renewable energy sources, batteries play an important role in load–power balancing in a DC microgrid. The existing energy management strategy may be able to meet the load demand. However, that technique is not suitable forrural communities’ power system structure. This research offers an energy management strategy (EMS) for a DC microgrid to supply power to rural communities with solar, wind, fuel cell, and batteries as input sources. The proposed EMS performs the load–power balancing between each source (renewable and storage) in a DC microgrid for dynamic load variation. Here, the EMS handles two battery sources (one is used to deliver power to the priority load, and the other is utilized in the common DC bus) to meet the required demand. The proposed EMS is capable of handling load–power balancing using renewable energy sources with less consumption of non- conventional energy sources (such as a diesel generator). The performance of the system is analyzed based on different operating conditions of the input sources. The MATLAB/Simulink simulation model for the proposed DC microgrid with their EMS control system is developed and investigated, and their results are tabulated under different input and load conditions. The proposed EMS is verified through a laboratory real-time DC microgrid experimental setup, and the results are discussed.

In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred.

Distribution systems can form islands when faults occur. Each island represents a subsection with variable boundaries subject to the location of fault(s) in the system. A subsection with variable boundaries is referred to as an island in this paper. For operation in autonomous mode, it is imperative to detect the island configurations and stabilize these subsections. This paper presents a novel scheme for the detection of island boundaries and stabilizing the system during autonomous operation. In the first stage, a boundary detection method is proposed to detect the configuration of the island. In the second stage, a dynamic voltage sensitivity factor (DVSF) is proposed to assess the dynamic performance of the system. In the third stage, a wide area load shedding program is adopted based on DVSF to shed the load in weak bus-bars and stabilize the system. The proposed scheme is validated and tested on a generic 18-bus system using a combination of EMTDC/PSCAD and MATLAB software’s.

In this article a new Transformer and Switched Capacitor-based Boost Converter (T & SC-BC) is proposed for high-voltage/low-current renewable energy applications. The proposed T & SC-BC is an original extension for DC-DC boost converter which is designed by utilizing a transformer and switched capacitor (T & SC). Photovoltaic (PV) energy is a fast emergent segment among the renewable energy systems. The proposed T & SC-BC combines the features of the conventional boost converter and T & SC to achieve a high voltage conversion ratio. A Maximum Power Point Tracking (MPPT) controller is compulsory and necessary in a PV system to extract maximum power. Thus, a photovoltaic MPPT control mechanism also articulated for the proposed T & SC-BC. The voltage conversion ratio (Vo/Vin) of proposed converter is (1 + k)/(1 − D) where, k is the turns ratio of the transformer and D is the duty cycle (thus, the converter provides 9.26, 13.88, 50/3 voltage conversion ratios at 78.4 duty cycle with k = 1, 2, 2.6, respectively). The conspicuous features of proposed T & SC-BC are: (i) a high voltage conversion ratio (Vo/Vin); (ii) continuous input current (Iin); (iii) single switch topology; (iv) single input source; (v) low drain to source voltage (VDS) rating of control switch; (vi) a single inductor and a single untapped transformer are used. Moreover, the proposed T & SC-BC topology was compared with recently addressed DC-DC converters in terms of number of components, cost, voltage conversion ratio, ripples, efficiency and power range. Simulation and experimental results are provided which validate the functionality, design and concept of the proposed approach.

Considering the aim of having low switching losses, especially in medium-voltage and high-power converters, the pre-programmed pulse width modulation technique is very useful because the generated harmonic content can be known in advance and optimized. Among the different low switching frequency techniques, the Selective Harmonics Elimination (SHE) modulation method is most suitable because of its direct control over the harmonic spectrum. This paper proposes a method for obtaining multiple solutions for selectively eliminating specific harmonics in a wide range of modulation indices by using modified Newton–Raphson (NR) and pattern generation techniques. The different pattern generation and synthesis approach provide more degrees of freedom and a way to operate the converter in a wide range of modulation. The modified Newton–Raphson technique is not complex and ensures fast convergence on a solution. Moreover, multiple solutions are obtained by keeping a very small increase in the modulation index. In the previous methods, solutions were not obtainable at all modulation indices. In this paper, only exact solutions to the low-order harmonics elimination for Cascaded H-bridge inverter are reported for all modulation indices. Analytical and simulation results prove the robustness and correctness of the technique proposed in this paper.

Simultaneous power transfer to multiple receiver (Rx) system is one of the key advantages of wireless power transfer (WPT) system using magnetic resonance. However, determining the optimal condition to uniformly transfer the power to a selected Rx at high efficiency is the challenging task under the dynamic environment. The cross-coupling and frequency splitting are the dominant issues present in the multiple Rx dynamic WPT system. The existing analysis is performed by considering any one issue present in the system; on the other hand, the cross coupling and frequency splitting issues are interrelated in dynamic Rx’s, which requires a comprehensive design strategy by considering both the problems. This paper proposes an optimal design of multiple Rx WPT system, which can eliminate cross coupling, frequency splitting issues and increase the power transfer efficiency (PTE) of selected Rx. The cross-coupling rejection, uniform power transfer is performed by adding an additional relay coil and independent resonance frequency tuning with capacitive compensation to each Rx unit. The frequency splitting phenomena are eliminated using non-identical transmitter (Tx) and Rx coil structure which can maintain the coupling between the coil under the critical coupling limit. The mathematical analysis of the compensation capacitance calculation and optimal Tx coil size identification is performed for the four Rx WPT system. Finite element analysis and experimental investigation are carried out for the proposed design in static and dynamic conditions.

Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method.

This paper presents a direct equilibrium tracing method for identifying the voltage collapse point of a self-excitedinduction generator (SEIG) without many trials. The technique solves differential and algebraic equations simultaneously toobtain the variables in a single step. The load parameter is also automatically varied during equilibrium tracing and thisreduces the computational time significantly. Comparing the simulation results obtained through conventional iterative procedure shows the effectiveness of the technique. An experimental verification on a 1.5KW induction machine validates the simulation results.

This paper presents an artificial neural network (ANN) based approach to tune the parameters of the cascaded d-q axis controller for an AC-DC-AC converter without dc link capacitor. The proposed converter uses the cascaded d-q axis controller on the rectifier side and space vector pulse width modulation on the inverter side. The feed-forward ANN with the error back-propagation training is employed to tune the parameters of the cascaded d-q axis controller. The converter topology provides simple commutation procedure with reduced number of switches and has additional advantages such as good voltage transfer ratio, four quadrant operation, unity power factor, no DC link capacitor and less THD in both the line and load sides. Simulation results closely match with theoretical analysis.