A wireless charging using wireless power transfer (WPT) technique is the future of electric vehicles (EVs). With the implementation of wireless charging, efforts on modifying the plug-in EV charging infrastructure compatibility could be reduced. WPT system basically works on the principle of electromagnetic induction. WPT has basic two types, inductive power transfer (IPT) and magnetic resonance coupling (MRC) power transfer. For a conventional IPT, two-stage structure is one of the most common topologies, of which the efficiency will be dramatically decreased by a cascaded dc–dc converter. To overcome the issue, in this paper, various partial power processing (PPP) converter (PPPC) architectures for wireless chargers are shown and a detailed analytical evaluation of S-S and LCL-S compensated topologies is given. With PPP based architectures, the system efficiency is improved, and the converter size is reduced. The WPT receiver is divided into the main receiver and the auxiliary receiver, i.e. dual receiver. Most of the power is transferred to the load through the main receiver, whereas the auxiliary receiver is used to regulate the output voltage. As a result, the switching loss and device stress of the dc–dc cascaded at receiver can be significantly reduced.

The on-board charger (OBC) of an electric vehicle (EV) is desired to perform many functions, such as transferring power from the grid to the vehicle (G2V), transferring power from the vehicle to the grid (V2G), and so on. To meet these requirements, a topology based on a four-switch three-port converter is proposed in this paper. In addition to the G2V and V2G functions, the proposed converter can transfer power from the high-voltage battery to the auxiliary low-voltage battery (H2L). Compared to the traditional OBC topology, the proposed circuit has a lower number of switches, reducing the cost of the system. Simulation results using PLECS software to demonstrate the effectiveness of the proposed topology.

Reducing the battery charging time of an electric vehicle (EV) is one of the key factors to boost the widespread adoption of EVs. The commercial, off-board high power, dc fast charging station need high initial investment and maintenance cost. On the other hand, the standard on-board type-1 and type-2 ac chargers with$3.3~kW$to$19~kW$need long time to charge. This paper proposes a combinatory ac and dc charging approach to increase the charging rate of EV batteries. The proposed combinatory charging approach provides a technique to charge EV battery from the on-board type-2 ac charger and drivetrain integrated dc charger. For drivetrain integrated dc charging, a dc input port$(N (+),O(-))$is formed using the neutral of the EV motor winding$(N)$and negative rail of the drivetrain inverter$(O)$. Through this dc input port, power from the renewable energy source-based dc microgrids, solar rooftops and other EV battery can be accepted for charging. The EV drivetrain inverter is controlled as an integrated interleaved dc-dc converter (IDC) to receive power from dc sources with EV motor windings reutilized as filter inductors. The control scheme for regulating the voltage across common dc-link accepting power from type-2 ac charger and integrated interleaved dc charger is presented. The performance analysis of EV motor and drivetrain integrated DC charger is validated through Finiet Element methods (FEM) co-simulation using Ansys Maxwell and Simplorer. A scaled experimental prototype is developed to validate the proposed combined ac and dc charging approach.

The proliferation of electric vehicles (EVs), owing to their advantages over internal combustion engine vehicles, has introduced many challenges, due to the lack of charging infrastructure that can handle such a large surge of EVs. Therefore, alternative feasible charging solutions, such as EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence thanks to the bidirectional charger. However, there are several challenges hindering the adoption of V2V energy sharing solutions. The existing frameworks that detail the main aspects in energy management protocols emphasize solely on the EV integration with the grid, with the assumption of the grid availability and capability of supporting V2V energy sharing. In this article, a novel holistic energy management framework for an efficient V2V energy sharing is proposed. The proposed framework offers a complete overview of the different stages in the V2V charging problem and introduces possible solutions for each stage and its integration with other stages to form a comprehensive V2V solution, that is not only cost-effective but also maximizes user satisfaction and social welfare, while simultaneously fulfills the highest number of energy demands.

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

Electric Vehicles (EVs) are being widely adopted to completely replace vehicles with internal combustion engines. However, the development of charging infrastructure to support the growing number of EVs has been lacking primarily due to the high cost of installation. Additionally, the immature and non-uniform deployment of charging stations has led to the absence of charging infrastructure in areas such as highways and rural areas. As a result, emerging concepts such as, EV-to-Home (V2H), EV-to-Grid (V2G) and EV-to-EV (V2V) charging have gained prominence. In this paper, the V2V energy sharing concept is exploited, where an intelligent and comprehensive framework to manage and allocate energy between EVs is proposed. This work presents a realistic modeling of the V2V energy sharing problem and proposes a two-layer matching approach that can efficiently match the EVs. The proposed approach not only optimizes the cost, but also the time, system energy efficiency, user satisfaction, and social welfare; hence making the approach more realistic and inclusive. Gale-Shapley is utilized to produce stable matchings, in the first layer, whereas a user-satisfaction model is devised to ensure realistic matchings, in the second layer. Additionally, a real-life dataset is developed from commercially available EVs and the performance of the system is evaluated using this dataset, in addition to realistic parameters derived from real-life data. The proposed approach is compared with a benchmark, where the results show that efficient, realistic, and effective V2V matches are achievable, paving the way for the adoption of such framework.

This paper presents a solar power generation prediction technique using artificial neural network. The predicted data is then applied to the adaptive power management strategy for Photovoltaic (PV) generation units in a standalone microgrid. The intermittent nature of solar power generation leads to major challenges in power system planning and load sharing. Prediction of solar power generation based on weather conditions and the proper use of this data in power management strategies improve the performance of existing standalone systems. This paper proposes an adaptive control strategy, which uses the predicted value of solar generation to determine the mode of operation. An ANN model is developed and trained using the dependency of solar power generation on weather parameters. The trained model is used to predict the expected solar power generation at any time. The applicability of the proposed adaptive control method is analyzed using Matlab/Simulink based simulation studies.

This paper proposes an adaptive power management strategy for standalone microgrid with PV and diesel generation. System security in a microgrid becomes challenging due to the intermittent power generation of PV units. Battery storage and diesel generation have been incorporated in the microgrid to allow continuous supply of power to the customers. The existing power management strategies mainly focus on PV/battery hybrid system. In this paper, a novel adaptive power management control algorithm is developed to achieve smooth transition between different generation units. The proposed adaptive control strategy is analyzed by Matlab/Simulink based simulation studies.

In an islanded microgrid, harmonic current is controlled (or shared) by modifying the effective impedance in the path of harmonic current. This effective impedance includes line impedance and the internal impedance of the generation unit. Reported works consider the aforementioned impedances as separate entities and their combined effect in harmonic current flow has not been discussed so far. This paper analyses the existing techniques and proposes a comprehensive harmonic current control technique, considering both line impedance and the impedance of the generation unit. The proposed technique has been tested using simulation as well as experimental studies.

Abstract Accuracy is still the greatest challenge in the wireless sensor network localization efforts. Several diverse factors can give rise to localization errors. Modeling such diverse influencing factors to deliver a single, reasonably simple and practical solution is a difficult task. In order to address the problem of location inaccuracy, we propose a comparatively simple and ingenious approach, which is the simultaneous perturbation stochastic approximation (SPSA) localization engine. SPSA bypasses tedious modeling of the influencing factors where some of them are yet to be explored and random in nature. SPSA‐based localization estimates the non‐anchor node locations through minimizing the summation of estimated errors of all neighbors. However, the downside of SPSA is that it incurs errors in some specific relative neighborhood configurations often referred to as flip ambiguity. So, we further propose a solution to the flip ambiguity problem by implementing a constrained optimization with a penalty function method on the identified flip nodes. Most importantly, error propagation of the iterative localization algorithm is managed by incorporating a neighbor confidence matrix. We name this modified SPSA engine as simultaneous perturbation stochastic approximation by neighbor confidence (SPSA‐NC). Experimental results show that SPSA‐NC offers significantly better localization accuracy than its state‐of‐the‐art competitors, namely, simulated annealing and the ordinary SPSA. The SPSA‐NC program is available for downloading at http://www.dnagroup.org/SPSANC . Copyright © 2015 John Wiley & Sons, Ltd.

Harmonic power sharing is important in islanded microgrid due to the presence of excessive nonlinear loads in distribution network. The lack of effective harmonic power sharing techniques may lead to overloading of certain distributed generation (DG) units. The concept of variable negative virtual harmonic impedance to share non-linear load in an islanded microgrid has been proposed recently. This paper presents a negative virtual harmonic impedance technique to share nonlinear load in a low voltage microgrid with complex line impedance. A harmonic droop control based on harmonic output power and the virtual output impedance of the DG is implemented to achieve non-linear load sharing. MATLAB/Simulink based simulation studies are presented to validate the non-linear load sharing capability of the proposed strategy.

This paper proposes a novel control strategy for harmonic current filtering in an inverter-based-islanded microgrid, to minimize harmonic propagation in the distribution network. In most of the reported works, the nonlinear load is shared among the distributed generation units by altering either the effective line impedance or the inverter output impedance. In the proposed control scheme, the DG, which is in close proximity to the nonlinear load, is controlled to support the maximum harmonic load demand. In an isolated power system, with decentralized control, all distribution units operate in voltage controlled mode to share active and reactive power. In the proposed control, harmonic current filtering is achieved by controlling the harmonic content of inverter output current. Thus, the proposed control scheme controls the inverter output voltage at fundamental frequency and the inverter output current at harmonic frequencies. The independent control of linear and nonlinear components offers maximum flexibility of power distribution. The feasibility of the proposed scheme is demonstrated using Matlab/Simulink based simulation studies.

This paper deals with the issues related to the interconnection between the microgrid and main network. The Dynamic Voltage Restorer (DVR) is proposed as an interface between the main network and microgrid in order to improve reliability and limit fault currents. The performance of DVR is demonstrated during fault conditions in two different locations, firstly in the main grid and then within the microgrid. The DVR is used to inject a voltage during a fault in the main grid isolating the microgrid from the fault and ensuring normal operation. Secondly, a novel technique is proposed employing the magnetizing inductance of the DVR series transformer in order to minimize the contribution of the grid currents during a fault within the microgrid. The proposed technique is evaluated using MATLAB/SIMULINK and shown to possess additional advantages when compared to traditional fault current limiters.

Artificial neural network (ANN) is becoming an attractive estimation and regression technique in many control applications due to its parallel computing nature and high learning capability. There has been a lot of effort in employing the ANN in shunt active power filter (APF) control applications. Adaptive Linear Neuron (ADALINE) and feed-forward multilayer neural network (MNN) are the most commonly used ANN techniques to extract fundamental and/or harmonic components present in the nonlinear currents. This paper aims to provide an in-depth understanding on realizing ADALINE and feed-forward MNN-based control algorithms for shunt APF. A step-by-step procedure to implement these ANN-based techniques in MATLAB/Simulink environment is provided. Furthermore, a detailed analysis on the performance, limitation, and advantages of both methods is presented in the paper. The study is supported by conducting both simulation and experimental validations.

This paper proposes an adaptive control for grid connected photovoltaic (PV) system that allows for the active power injection (API) and active power filtering (APF) functionality while allowing the maximum utilization of the PV inverter. This PV system operates in three modes: (i) API only, (ii) API and APF with the full utilization of PV inverter VA capacity, and (iii) API and APF with specific total harmonic distortion (THD) limit for the injected currents. The maximum power point tracking (MPPT) is achieved with perturb and observe method. The proposed algorithm calculates the peak fundamental, harmonic current and then as per the available PV inverter capacity, modifies this value for the full utilization of PV inverter. Adaptive linear neuron (ADALINE) based online Fourier analysis is used to estimate the fundamental and harmonics components of the load and injected currents. The operation of the single stage PV inverter system is analyzed with the proposed control modes for two different atmospheric and load conditions.

This paper presents a review of the most recent module integrated converters (MIC) for PV applications. The MIC topologies are classified in to three main categories: (1) MIC with DC link; (2) MIC with current-unfolding; (3) DC link-less MIC. The study compares various topologies in-depth with respect to efficiency, compactness, reliability, cost effectiveness and ease of implementation. Based on the comparison, several topologies are recommended to be the potential candidates for practical implementation. Simulation is adopted to support the analysis and comparison.

This paper proposes a stationary-frame control method for voltage unbalance compensation using Interline Photovoltaic (I-PV) power system. I-PV power systems are controlled to compensate voltage unbalance autonomously. The control system of I-PV plants mainly consists of active and reactive power droop controllers, voltage and current controllers and unbalance compensator. This approach is based on injecting negative sequence current from I-PV power plants to compensate for the unbalanced loads. Consequently, the Point of Common Coupling (PCC) voltage remains balanced and regulated. The results obtained from Matlab/Simulink based simulation demonstrate the effectiveness of the proposed method in compensation of voltage unbalance at PCC.

This paper addresses implementation issues associated with a novel damping control algorithms for STATCOM and SSSC (static synchronous series compensator) in a series compensated wind park for mitigating SSR (subsynchronous resonance) and damping power system oscillations. The IEEE first benchmark model on subsynchronous resonance is adopted with integrating aggregated self-excited induction generator based wind turbine to perform the studies. The potential occurrence and mitigation of the SSR caused by induction generator effects as well as torsional interactions, in a series compensated wind park are investigated. The auxiliary subsynchronous damping control loops for the STATCOM and SSSC based on a novel design procedure of nonlinear optimization are developed to meet the damping torque in the range of critical torsional frequencies. The performances of the controllers are tested in steady state operation and in response to system contingencies, taking into account the impact of short circuit ratios (SCRs). Simulation results are presented to demonstrate the capability of the controllers for mitigating the SSR, damping the power system oscillation and enhancing the transient stability margin in response to different SCRs.