Search

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.

A novel distributed real-time energy management strategy for inverter-based microgrids based on dynamic algorithms with a low computational burden and without the requirement of offline forecast or a central optimizer is presented in this paper. A dynamic economic dispatch problem is solved while a stability performance is guaranteed. The algorithm is able to dynamically adjust the power references under sudden load changes, generator disconnection, and communication failures. In steady-state, the proposed real-time energy management strategy guarantees optimality conditions and frequency regulation. Finally, the effectiveness of the proposed strategy and its compatibility with the conventional proportional-resonant inner controllers and primary droop controllers are validated in experiments.

Transformer-less inverters (TIs) are widely used in photovoltaic (PV) applications owing to their low cost and high efficiency. However, the combined effect of high-frequency common mode voltage and PV parasitic capacitance burdens the TIs with undesired ground leakage current. In this article, a switched capacitor (SC)-based five-level TI is proposed. The negative dc rail of the proposed TI is directly connected to the grid neutral, thereby completely eliminating the leakage current. Besides, it has the inherent capability of boosting the output voltage without using any magnetic component or additional circuitry. For several instances in every fundamental cycle, the SCs are paralleled with the input voltage source and hence are self-balanced. The proposed topology does not have any limitation on modulation index since the SC voltages are self-balanced and hence less complex or no dedicated control scheme is required. Further, a quantitative comparative study is presented, highlighting the benefits of the proposed TI regarding boosted voltage, elimination of leakage current, and reduced component count. Finally, in order to verify the performance of the proposed TI, detailed simulations followed by experiments on a laboratory prototype developed by using DS1202/1302 dSPACE controller are carried out and the results presented for both steady-state and dynamic conditions verify the feasibility of the proposed TI.

This Editorial Submission is for SS on Deep Learning and Data Analytics to Support the Smart Grid Operation with Renewable Energy. Out of 81 submissions in SS, only 12 have been finally accepted to be published in this Transactions.

The main focus of this paper is to present the current prospects, potentials, research activities, future concerns, and applications of solar photovoltaic (PV) systems in Bangladesh. Despite the existence of several articles in the literature on solar energy prospects in Bangladesh, the diffusion of several solar PV applications throughout the country, their rapid progress analysis, and existing feasibility study outcome analysis are still unrevealed. Therefore, an endeavour has been made for the first time to condense the government and non‐government contributions to disseminating solar energy throughout the country. Twenty‐one feasibility studies at different locations of Bangladesh are reviewed and analysed, in terms of financial viability, sensitivity analysis, and greenhouse gas emissions, to foresee the prospects of solar energy in Bangladesh. Sensitivity analyses are performed, with six sensitivity variables, which reveal that the total net present cost of the solar project implementation may be increased by 20% (maximum) in the lowest solar potential area than the highest one with an average payback period of 13 years. This paper also identifies the policy gaps, classifies the barriers into eight categories, and discusses the conceivable approaches to defeat those barriers, followed by recommendations in six different aspects.

The advent of smart power grid, which plays a vital role in the upcoming smart city era, is accompanied with the implementation of a monitoring tool, called state estimation (SE). For the case of the unbalanced residential distribution grid, the state-estimating operation, which is conducted at a regional scale, is considered as an application of the edge computing-based Internet of Things (IoT). While the outcome of the SE is important to the subsequent control activities, its accuracy heavily depends on the data integrity of the information collected from the scattered measurement devices. This fact exposes the vulnerability of the SE module under the effect of data-driven attacks. Among these, the false data-injection (FDI) attack is attracting much attention due to its capability to interfere with the normal operation of the network without being detected. This article presents an attack design scheme based on a nonlinear physical-constraint model that is able to produce an FDI attack with theoretically stealthy characteristic. To demonstrate the effectiveness of the proposed design scheme, simulations with the IEEE 13-node test feeder and the WSCC 9-bus system are conducted. The experimental results indicate that not only the false-positive rate of the bad data detection mechanism is 100% but the physical consequence of the attack is severe. These results pose a serious challenge for the operators in maintaining the integrity of measurement data.

Some systems, in spite of having multiple outputs, have only one control input, which makes their control a challenge. Two novel controllers are proposed that utilise an adaptive finite‐time sliding mode control (AFSMC) scheme for a class of single‐input multiple‐output (SIMO) nonlinear systems in the presence of unknown mismatched uncertainties. To alleviate the inherent chattering phenomenon of sliding mode control, new forms of the two designed controllers are suggested by using new sliding surfaces. Not only can the proposed AFSMC scheme stabilise the system in a finite time, but also it can provide estimated data of the uncertainty upper bound in the controller. Lyapunov stability theory is used to obtain finite‐time stability analysis of the closed‐loop system. Finally, simulation results are carried out in Simulink/MATLAB for a four‐dimensional autonomous hyper‐chaotic system with mismatched uncertainties as an example of SIMO uncertain nonlinear systems to reveal the effectiveness of the proposed controllers.

The chaotic support structures for offshore wind turbines are often subjected to a severe environment. A robust control scheme needs to be considered to maintain them in a safe operational limit. Robust sliding mode control (SMC) scheme can provide an excellent robust controller against this severe and challenging environment for these chaotic structures. This paper proposes a novel fixed-time adaptive sliding mode control scheme with a state observer to synchronize chaotic support structures for offshore wind turbines in the presence of matched parametric uncertainties. The proposed controller is a new integration of adaptive control concept, SMC method, fixed-time stability concept and a state observer. A fixed-time stability concept is used to provide stability for the system within a presented time regardless of initial conditions. The adaptive concept is utilized to provide an online estimator of the uncertain upper bound. Also, a nonlinear observer is employed to provide an online estimator of an unmeasured state in the controller. Lyapunov stability theorem is used to analyze fixed-time stability of the system based on SMC methodology. The simulation results demonstrate that the proposed controller is able to ensure fixed-time synchronization along with providing precise means to estimate the unmeasured state as well as uncertainty upper bound.

Nowadays, transformer‐less Photovoltaic‐based grid‐connected inverters are more popular in renewable energy application due to their reduced size, cost, and high efficiency. However, the absence of galvanic isolation leads to the flow of leakage current in such system hindering personal safety, and deteriorating power quality. Therefore, this paper presents a five‐level transformer‐less inverter topology for PV applications with less component count and reduced complexity. The proposed inverter topology completely eliminates the common mode leakage current. Besides, the proposed topology has an inherent capability to boost the voltage without using any magnetic component or boosting circuit, and is based on the principle of switched/auxiliary capacitor. The proposed topology do not have any limitation of modulation index since the auxiliary capacitors are naturally balanced, hence less complex or no dedicated control scheme is required for balancing/regulating the auxiliary capacitor voltages. In order to verify the performance of the proposed inverter topology, a laboratory prototype is developed which confirms the operation, and feasibility of the proposed inverter. Results depicting the output voltage, load current, and voltage across the auxiliary capacitor are presented for various operating conditions. Further, simulation results depicting the leakage current in the proposed, H5, and H6 topologies are included. Finally, a quantitative comparison study is done, highlighting the benefits of the proposed inverter topology regarding boosted voltage, elimination of leakage current, and reduced component count.

This paper proposes and implements a model-free optimal strategy to regulate the voltage in distribution systems by effective control of Inverter-Interfaced Distributed Energy Resources (I-DERs) output power. In distribution systems, where the ratio of R/X is considerable, reactive power control is not always an optimum solution for voltage regulation. With the aim of improving voltage regulation, this paper proposes an alternative method to optimally coordinate active and reactive power of I-DERs in the grid. The proposed method does not need to have a system model and phase measurement, which are barriers in control and operation of behind-the-meter assets. The paper formulates the problem of voltage regulation in form of equilibrium-map-with-optimum, without any restriction on system configuration, amount of reverse power flow and losses. Using the extremum seeking control technique and without an explicit model of distribution circuit, the ratio of active and reactive power is coordinated in real time. The amount of exchanged apparent power is also adjusted simultaneously. Simulation results for a typical distribution circuit verify the effectiveness of the proposed approach in improving the voltage regulation performance. The experimental results also show a 20 percent reduction in use of system capacity to provide the desired voltage regulation performance.

This study investigates a new chattering-free robust predefined-time sliding mode control (CFRPSMC) scheme for the trajectory tracking control problem of a three-degree-of-freedom (3-DOF) remotely operated vehicle (ROV) in the presence of matched uncertainties. The advanced notion of predefined-time stability is used to provide a maximum convergence time as desired that can be set during the control design and independently of the initial conditions. Based on defining a new form of sliding surfaces, a new control law is designed to ease the undesirable chattering phenomenon without damaging the robustness properties and tracking precision. The proposed control scheme can not only solve the predefined-time tracking controller design problem, but also provide the robustness to various uncertainties. The Lyapunov stability theory is used to establish the stability analysis of the closed-loop system in both the reaching phase and the sliding phase. The performance of the proposed CFRPSMC scheme is evaluated for the 3-DOF ROV through two comparative simulation cases using Simulink/MATLAB. The comparative simulation results and analytical comparisons demonstrate the efficacy and superiority of the proposed method compared with other relevant conventional methods.

With the recent rollout of smart meters, huge amount of data can be generated on hourly and daily basis. Researchers and industry persons can leverage from this big data to make intelligent decisions via deep learning (DL) algorithms. However, the performance of DL algorithms are heavily dependent on the proper selection of parameters. If the hyperparameters are poorly selected, they usually lead to suboptimal results. Traditional approaches include a manual setting of parameters by trial and error methods which is time consuming and difficult process. In this paper, a Bayesian approach based on acquisition is presented to automatic selection of optimal parameters based on provided data. The acquisition function was established to search for the best parameter from the input space and evaluate the next points based on past observations. The tuning process identifies the best model parameters by iterating the objective function and minimizing the loss for optimizable variables such as learning rate and Hidden layersize. To validate the presented approach, we conducted a case study on real-life energy management datasets while constructing a deep learning model on MATLAB platform. A performance comparison was drawn with random parameters and optimal parameters selected by presented approach. The comparison results illustrate that the presented approach is effective as it brings a notable improvement in the performance of learning algorithm.

This paper proposes multi-timescale voltage stability-constrained volt/VAR optimization to improve the steady-state stability and security of smart distribution grids. It aims to coordinate the post-contingency slow-timescale corrective actions provided by an on-load tap changer with fast-timescale corrective actions provided by photovoltaic inverters and battery energy storage system. This multi-timescale operation exploits the difference between short- and long-term emergency ratings of line flows and voltage magnitudes usually defined by utilities for contingency cases. Global optimum is achieved by formulating the problem as mixed-integer second-order cone program. To justify the investment in battery storage, the proper coordination between base case state-of-charge and storage dynamic reserve margin required for post-contingency corrective actions is performed. Also, the steady-state voltage stability margin is computed through detailed voltage stability analysis. Simulation results demonstrate the effectiveness of the proposed multi-timescale corrective actions in maintaining the steady-state stability and security under contingency cases.

In this paper, impacts of cyber-attack on proportional integral derivative controlled automatic generation control unit and automatic voltage regulator have been analyzed individually. A controlled switching unit has been proposed for ensuring frequency stability during an attack. Also, another controlled switching unit, designed for automatic voltage regulator, has been proposed for mitigating voltage disruption during a cyber-attack. The protective model proposed in this paper offers combined protection for frequency and voltage disruptions in a power system during cyber-attack.

This paper presents second-order cone programming (SOCP) and semidefinite programming (SDP) models to solve the multiperiod optimal control problem of unbalanced three-phase distribution grids with battery energy storage systems. The decision variables are the active and reactive power of the battery energy storage system. The objective is to minimize the power loss and energy purchase cost from the distribution substation. The optimal dispatch problem requires solution by volt/VAR optimization. The SOCP and SDP models ensure global optimum of original nonconvex nonlinear programming problem. Multiobjective volt/VAR optimization is performed and the benefits of reactive power support from battery energy storage systems are explored. A simulation-based heuristic to extract rank-one solution from the higher rank solution of SDP model is also proposed. Empirical results show the numerical stability and exactness of the proposed three-phase SOCP and SDP models.

This paper presents a switching frequency sensitivity analysis of the dc microgrid connected dc-dc voltage source converters. A multi-frequency averaging based dynamic phasor model has been developed to analyses the switching frequency dependency of power electronic interfaces. The voltage source converter’s dynamical model includes the transmission line current dynamic between the dc-dc voltage conversion interface and the common dc bus. The switching frequency dependency of the converter has been investigated under 1 and 10 kHz frequency in electromagnetic transient (Simulink), state-space averaging and multi-frequency averaging platform.

This paper presents nonlinear partial feedback linearized control scheme for AC microgrid connected DC-AC voltage source converters. The feedback linearization approach partially linearizes the nonlinear dynamical model of the DC-AC converters. A standard PD controller is applied to the linearized model to regulate the output voltage of the distributed generations. The proposed controller performance is illustrated under two different scenarios: demand load change in common AC-bus, and another one is three-phase to ground short-circuit fault.

This letter presents experimental results showing significant enhancement in the dynamic performance of a novel piezoelectric micro-lens actuator using a robust feedback resonant controller. The design of the feedback controller is based on experimentally identified model of the micro-lens actuator. The stability of the closed-loop system is proven by the negative imaginary theory. The experimental results show that the resonance peak and the settling time of the actuator are reduced by 27 dB and to 2.5 ms from 119 ms, respectively. Such improvement demonstrates the capability of the piezoelectric micro-lens actuator for fast auto-focus application in miniaturized camera. The reported performance makes it the fastest micro-lens actuator compared to other previously reported micro-lens actuators. [2020-0313]

This paper presents the development of a dynamic analytical model for piezoelectrically driven micro-lens actuator for micro-optics applications. The model uniquely considers the effect of residual stress in the thin films constituting the micro-actuator and the actuating DC offset voltage of the micro-lens actuator. Euler-Bernoulli beam theory is used to formulate the dynamic model. Experiments were performed on the fabricated piezoelectric micro-lens actuator to validate the dynamic analytical model. The results show that the measured resonance frequency of 927 Hz and change in resonance frequency of 4 Hz/V for actuating DC voltage are in good agreement with the analytical dynamic model prediction of resonance frequency 948 Hz and shift in resonance frequency 4.5 Hz/V. This model allows one to predict the dynamic behavior of the micro-lens actuator under the residual stress and actuating DC voltage.

The acquisition of a high-quality imaging performance by an atomic force microscope is significantly influenced by the motion coupling effect of its key scanning unit, i.e. its piezoelectric tube scanner. In this Letter, to improve its performance, a MIMO model predictive control scheme for reducing this effect is proposed. The proposed controller achieves this by greatly overcoming the problem of tilted characters in the atomic force microscopy (AFM) scanned images. The experimental results are demonstrating the effectiveness of the proposed control technique.

In this study, the significant effects of the load models used to analyse low-voltage power systems are presented and a systematic process for selecting appropriate load compositions to evaluate the performance of distribution systems containing distributed generation is proposed. The driving force behind, and key reasons for, voltage instability are analysed by evaluating different load models subjected to grid disturbances. The stability conditions of distribution systems are also checked through time-domain simulations performed on various network configurations, such as radial, loop, and mesh topologies. The system's responses to changes in the substation's voltage and contingency events are also demonstrated. It is found that the types and order of load models used greatly affect a system's voltage stability and therefore, an accurate load modelling is necessary to support the renewables integration in distribution systems. As the system's response is found to be sensitive to the network's topology and actual load composition, an approach that selects appropriate load compositions which, in turn, reduces the huge investment costs of improper planning, is proposed. The effectiveness of the proposed approach is verified by connecting static synchronous compensators to the network under varying load compositions.

This paper presents a robust nonlinear decentralized control scheme for islanded dc microgrids, where the main control objectives are to achieve the desired voltage at the common dc bus and to maintain the power balance. The proposed control scheme uses the partial feedback linearization scheme to simplify the dynamical models of different components in dc microgrids. In this paper, the dc microgrid includes a solar photovoltaic unit, a fuel cell system, and a battery energy storage system along with dc loads. The robustness of the proposed controller against parametric uncertainties is ensured by the parameter, appearing in the control inputs as unknown, which is then estimated through adaptation laws. The inherent noise decoupling capability of the feedback linearization scheme is used to provide robustness against external disturbances in dc microgrids. The performance of the proposed controller is evaluated on a dc microgrid through simulation and experimental studies in order to demonstrate the effectiveness and robustness under different operating conditions, while considering the effects of parametric uncertainties and external disturbances. Simulation results are also compared with an existing proportional-integral controller.

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are revised according to their specific roles to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

In this paper, a robust controller for the positioning of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM) is proposed. A minimax linear quadratic Gaussian (LQG) controller is designed based on an uncertain system model which is constructed by measuring modeling error between the measured and model frequency response. This controller is robust against uncertainties introduced as a result of spillover dynamics of the scanner at frequencies higher than the first resonance frequency (900 Hz) of the scanner and the variation of plant transfer function due to temperature, humidity, and duration of operation. The proposed controller is applied to the PTS in the AFM used in the experiments to evaluate the performance of the proposed method. It is observed that the proposed scheme provides up to 12 dB closed-loop damping of the resonant mode to track the reference triangular signal. The robust performance of the proposed controller has been investigated for 0–1.96 g sample mass variation. A high positioning accuracy up to 125 Hz frequency is achieved by reducing scanner's vibration and tracking error. Higher-quality imaging up to 125 Hz scanning frequency is achieved compared to the existing PI controller and some other existing methods. This control technique may be applied to control vibration for the systems with changing frequency response due to uncertainties, such as vibration control of disc-drive system. Refereed/Peer-reviewed

This paper presents methods to find a positively invariant set (PIS) and derive conditions on the edge weights for a multirate Kuramoto oscillator network to achieve synchronization. These methods can be applied to a network with an arbitrary topology and nonidentical oscillators. The proposed methods are based on the construction of energy functions for this type of network. Two different conditions on the edge weights are provided by using graph spectral properties, or alternatively by analyzing a path set of a graph. These methods provide flexibility in checking the edge weight conditions. We further improve the estimate of the PIS for the multirate Kuramoto network when its damping coefficients are greater than a certain value. The effectiveness and conservativeness of the proposed methods are demonstrated by simulation studies. A comparison with another method from the existing literature shows that our method gives less conservative estimate of the PIS.

This paper presents the design of a high-performance second-order controller for voltage control of a single- and three-phase islanded microgrid under different loads and fault conditions. The microgrid consists of several distributed generation units and local loads. The load is parametrically unknown and uncertain that produces the variable nature of microgrid performance. The controller is designed to achieve robust performance in the presence of unmodeled loads, dynamic loads, harmonic loads, balanced, and unbalanced loads. The design of the controller is presented using the results of robust negative imaginary approach. The performance evaluation of the controller shows that the proposed controller provides extensive control of voltage and current against a set of uncertainties and loads.

An efficient maximum power point tracking (MPPT) method plays an important role to improve the efficiency of a photovoltaic (PV) generation system. This study provides an extensive review of the current status of MPPT methods for PV systems which are classified into eight categories. The categorisation is based on the tracking characteristics of the discussed methods. The novelty of this study is that it focuses on the key characteristics and eleven selection parameters of the methods to make a comprehensive analysis, which is not considered together in any review works so far. Again, the pros and cons, classification and immense comparison among them described in this study can be used as a reference to address the gaps for further research in this field. A comparative review in tabular form is also presented at the end of the discussion of each category to evaluate the performance of these methods, which will help in selecting the appropriate technique for any specific application.

This study, based on a novel control strategy, proposes a sizing method for battery energy storage systems (ESSs), which makes the wind power system more dispatchable. The main objective of the proposed control-based sizing method is to facilitate robust unit commitment by smoothing the output power of wind according to a desired reference. Owing to the energy conversion loss, the controller closely monitors the battery state of charge (SOC) to prevent the battery from being fully discharged during the smoothing procedure. The proposed controller can automatically revise the battery dispatching reference maintaining the SOC constant. In each dispatch interval, model predictive control is used in real time to ensure that the output power follows the generated reference. The ESS is sized according to the simulation results solved by the proposed control strategy. Meanwhile, the performance of the proposed control strategy is validated over actual wind power data.

A storage system is a key component of a microgrid. Over the last few years, research has been undertaken to determine optimal management of microgrid resources. Battery storage has a significant impact on the total operational cost as the lifetime of the battery reduces during charging and discharging cycles. In this paper, we propose optimal energy management of a community microgrid in which the cost function includes the degradation cost of the battery and a dynamic penalty to reflect the true operational cost. Particle swarm optimisation (PSO) is used to determine the battery control actions for real-time energy management. Several case studies are presented to demonstrate the effectiveness of the proposed framework in which the new cost function reduces electricity cost by up to 44.50% compared to a baseline method and 37.16% compared to another existing approach.

The challenges and potential solutions in modeling and simulation for the new distribution grids with high penetration of inverter interfaced distributed generators including wind and photovoltaic generators are presented in this paper. A dq0 dynamic model for grid-connected Current Source Inverters (CSI) with power control is developed and validated in two different simulation studies. In this way, Electromagnetic transient (EMT) and phasor (PS) simulations are performed for a simple case of a CSI connected to the grid. A modified 13-nodes IEEE distribution test system with photovoltaic and wind based distributed generation is performed to extend the results to a standard distribution test system. Accuracy and reliability problems can be inferred from the simulation results if the network dynamics are neglected, and the need to develop new simulation models for the new emerging distribution grids is shown.

This paper presents a feasibility and sensitivity analysis of renewable energy-based off-grid and grid-connected microgrids by investigating the potentials of wind and solar energy at different areas, namely, Kuakata, Sitakunda, Magnama, Dinajpur and Rangpur in Bangladesh - a country that experiences a tropical climate. A specialized neural network algorithm has been employed to track the wind speed and solar irradiance all year round in two salient regions and the promising results have been analyzed for making the decision whether the data are reliable for forecasting or not. Four different types of models including PV-Grid, Wind-Grid, Wind-PV-Grid, and off-grid hybrid renewables are designed using the Hybrid Optimization of Multiple Energy Resources (HOMER Pro) software. By considering the key factors: net present cost, cost of energy, renewable fraction, local load demand, availability of renewable energy resources, system economics and greenhouse gas emissions, the optimal hybrid renewable energy system (HRES) configurations (Wind/PV/Grid/Battery) for the mentioned regions are determined. Various sensitivity and optimization variables, such as RE resources, local load demand, grid energy price, nominal discount rate, the life-time of wind turbine, the capacity of wind turbine, PV arrays, converter, and battery are used to make the decision. Detailed sensitivity analyses are performed to investigate how the optimal system configurations change with a tiny variation in input variables and results show output results are more sensitive on the variations in long-term average wind speed and solar irradiance, nominal discount rate, and the lifetime of wind turbines than the other inputs which is definitely a vital finding of this investigation. Finally, considering several decision making factors, a detailed feasibility chart is presented for two distinct nominal discount rates, i.e., 9% and 10%, which depicts the economically viable renewable energy based plant size in the mentioned regions. Although the crux of this paper is based on providing low-cost electricity to people living in rural areas of Bangladesh, our propositions carry with them certain concomitant benefits, not least of which are environmental and social benefits.

An energy storage system is a key element of renewable-based power generation. Its flexible operational capabilities reduce not only the impact of intermittent power generation but also operational costs. In this paper, a dynamic penalty function is proposed to the charging term of the cost function to efficiently manage the battery energy and thereby reducing operational costs. The charging/discharging periods of the battery are effectively controlled based on the solar power generation and residential real-time electricity prices (RRTP). The optimisation problem formulated for the application of real-time energy management is solved with the help of particle swarm optimisation (PSO). It is shown that the proposed cost function can reduce operational costs over a time horizon of 96 hours by 4.2 per cent as compared to the cost function reported in the literature. Simulation studies are carried out to demonstrate the effectiveness of the proposed cost function over the existing cost function.

This paper presents a mixed-sensitivity-based robust H∞ loop-shaping partial feedback linearized control scheme to enhance the transient stability of a battery energy storage-associated standalone solar photovoltaic system. The proposed control scheme has been provided independent operating points for a generalized nonlinear dynamical model of DC microgrids connected standalone hybrid solar photovoltaic and battery energy storage system. A parametric uncertainty model is developed for the generalized dynamical model, and the noise disengaging merit of the proposed control technique has been investigated. The designed controller’s performance has been demonstrated under four different scenarios, and it is compared with the conventional PI controller for partial feedback linearized control law.

This paper proposes a novel flying capacitor (FC) based inverter for a grid-connected photovoltaic (PV) system. It is possible to connect the grid neutral directly with the PV negative bus; thereby, the topology exhibits an inherent zero leakage current. The devised topological structure is such that it can be implemented using industry-standard half-bridge modules, making it an industry-ready solution. A simple unipolar pulsewidth modulation is applied with the aid of a simple logic gate based gating pulse generation scheme. Further, the capacitance value of the FC is low owing to the cyclic charging of FC in every switching period. The operating principle with the proposed modulation scheme is detailed, and simulation results validating the operability are presented. Finally, through a comprehensive comparative study, the key advantages of the proposed topology are highlighted.

This paper a proposes a five-level transformer-less inverter based on common-ground-type topology is presented. In the common-ground-type utility neutral is directly tied with negative terminal of dc source, leads to the short-circuiting of PV-to-ground parasitic capacitance. Hence it solves variable common mode voltage and leakage current issues. Additionally, this topology has the inherent voltage boosting ability based on the principle of switched/auxiliary capacitor. A simplified logic gate-based PWM strategy and control algorithm is developed to maintain the voltage across auxiliary capacitor. A laboratory prototype is developed which proves the feasibility and merits of proposed inverter topology are presented.