Grid-connected photovoltaic (PV) systems are now a common part of the modern power network. A recent development in the topology of these systems is the use of transformerless inverters. Although they are compact, cheap, and efficient, transformerless inverters suffer from chronic leakage current. Various researches have been directed toward evolving their performance and diminishing leakage current. This paper introduces the application of a model predictive control (MPC) algorithm to govern and improve the performance of a grid-tied neutral-point-clamped (NPC) 3-φ transformerless inverter powered by a PV panel. The transformerless inverter was linked to the grid via an inductor/capacitor (LC) filter. The filter elements, as well as the internal impedance of the grid, were considered in the system model. The discrete model of the proposed system was determined, and the algorithm of the MPC controller was established. Matlab’s simulations for the proposed system, controlled by the MPC and the ordinary proportional–integral (PI) current controller with sinusoidal pulse width modulation (SPWM), were carried out. The simulation results showed that the MPC controller had the best performance for earth leakage current, total harmonic distortion (THD), and the grid current spectrum. Also, the efficiency of the system using the MPC was improved compared to that using a PI current controller with SPW modulation.

The impact of exercise interventions on south Asians with type 2 diabetes (T2DM), who have a higher T2DM incidence rate compared to other ethnic groups, is inconclusive. This study aimed to systematically review the effect of exercise interventions in south Asians with T2DM. Five electronic databases were searched up to April 2017 for controlled trials investigating the impact of exercise interventions on south Asian adults with T2DM. The PEDro scale was used to assess the quality of the included studies. Eighteen trials examining the effect of aerobic, resistance, balance or combined exercise programs met the eligibility criteria. All types of exercise were associated with improvements in glycemic control, blood pressure, waist circumference, blood lipids, muscle strength, functional mobility, quality of life or neuropathy progression. The majority of included studies were of poor methodological quality. Few studies compared different types or dose of exercise. In conclusion, this review supports the benefits of exercise for south Asians with T2DM, although it was not possible to identify the most effective exercise prescription. Further studies of good methodological quality are required to determine the most effective dosage and type of exercise to manage T2DM in this population.

Wind Turbine Generator (WTG) has become one of the most popular renewable-based power generation that is broadly connected to electricity grids worldwide. Till the year 2019, the total WTGs installed worldwide reached about 650.8GW. The WTG is interfaced to the electricity grid through power electronic converters with a proper control algorithm to facilitate a smooth power delivery as well as maintaining the system voltage and frequency stability during wind intermittency. However, power grids are usually subjected to load expansion which affects the stiffness of the grid and hence its stability. A weak electricity grid exhibits voltage instability that may affect the performance of WTGs and in some cases may lead to serious damages to the wind turbines and the entire system. In this paper, superconducting magnetic energy storage (SMES) technology based on fuzzy logic controller is implemented to effectively resolve this issue and improve the overall performance of WTGs. Hysteresis current and fuzzy logic-based control system is proposed to control the energy exchange between the SMES coil and the investigated system. Results show the effectiveness of the SMES to improve the overall system performance and along with the fault ridethrough capability of the doubly-fed induction generator (DFIG).INDEX TERMS DFIG, SMES, Low voltage ride through, Wind energy, Weak grid.

Due to their small size, minimum cost, and great efficiency, photovoltaic (PV) grid-connected transformerless inverters have been developed and become famous around the world in distributed PV generators systems. One of the most efficient topologies of the transformerless inverter family is H5 topology. This inverter extracts a discontinuous current from the PV panel, which conflicts with the operation at maximum power point tracking (MPPT) conditions while the utilization factor of the PV degrades. This paper proposes improved H5 topology featuring a boost converter inserted in the middle between the PV panels and the H5 inverter. The design of the boost converter is planned to operate at continuous conduction mode to guarantee MPPT conditions of the PV. A new and simple off line MPPT algorithm is introduced and performance factors like efficiency and utilization factors of the proposed and convention H5 topology are compared. The simulation results indicate that the proposed system provides a preferable utilization factor and a simpler MPPT algorithm.

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.

The integration of wind energy systems into the electric grid has become inevitable despite the many problems associated with this integration. Most of these problems are due to variations in wind speed. The problems are for example oscillations in the power generated, which implies the lack of guarantee of obtaining the maximum energy and the ripple in the electromechanical torque of the generator. This work aims at mitigating these problems for wind energy conversion system-driven doubly-fed induction generator (DFIG), which is the main wind turbine utilized for energy applications. This mitigation is performed through direct reactive and active powers control of the DFIG using an artificial neural network. A DSP (Digital Signal Processor-dSPACE DS1104) was used to experimentally test the proposed strategy. The dynamic performances of the controlled generator are analyzed by using the designed intelligent control strategy in the case of variable wind speeds and upon sudden change of the active power demand. Based on the obtained experimental results, it can be said that the designed intelligent control strategy outperforms traditional methods like direct power (DPC) and vector control in terms of reducing the current harmonics, and torque ripples, and enhancing dynamic response.

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Developing countries need to make use of sufficient potential of PV power sources to cover the incremental demand of energy security. Though the PV-diesel microgrid system involving maximum supervising action as well as without having energy storage system can afford the continual power supply in the unelectrified rural area, it may not be circumstantially companionable because of the dependence on fossil-fuels and total dispatched energy cost [1][2]. Moreover, an individual PV system is an incomplete basis of electricity supplier due to the power instability produced by unpredictable solar irradiance and atmospheric temperature. Hence, MPPT is used commonly with PV solar systems to maximize power extraction from PV supply. Reference [3] presented an exhaustive literature review on on-line and off-line procedures for PV MPPT system. Reference [4] evaluated the application of Incremental Conductance, Perturb & Observe (P&O) MPPT procedure depending of European Efficiency Test EN 50530 that was specially contrived for Abstract: One of the major challenges for battery energy stowage system is to design a supervisory controller which can yield high energy concentration, reduced self-discharge rate and prolong the battery lifetime. A regulatory PV-Battery Management System (BMS) based State of Charge (SOC) estimation is presented in this paper that optimally addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and utilizes the Maximum Power Point Tracking (MPPT) scheme of the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. A case study (SOC estimation) is demonstrated as well to depict the efficiency (Error 0.082%) of the proposed model using real time data. The numerical simulation structured through real-time information concedes that the projected control mechanism is robust and accomplishes several objectives of integrated PV-BMS for instance avoiding overcharging and deep discharging manner under different solar radiations.