In vivo biomedical devices are one of the most studied applications for vibrational energy harvesting. In this paper, we investigated a novel high-displacement device for harvesting heartbeats to power leadless implantable pacemakers. Due to the location peculiarities, certain constraints must be respected for the design of such devices. Indeed, the total dimension of the system must not exceed 5.9 mm to be usable within the leadless pacemakers and it must be able to generate accelerations lower than 0.25 m/s2 at frequencies of less than 50 Hz. The proposed design is an electrostatic system based on a square electret of dimension 4.5 mm. It is based on the Quasi-Concertina structure, which has a very low resonant frequency of 26.02 Hz and a low stiffness of 0.492 N/m, allowing it to be very useful in such an application. Using a Teflon electret charged at 1000 V, the device was able to generate an average power of 10.06 μW at a vibration rate of 0.25 m/s2 at the resonant frequency.

This paper presents a comparative study between the conventional PI (Proportional Integral) and backstepping controllers applied to the DFIG (Doubly Fed Induction Generator) used in WECS (Wind Energy Conversion System). These two different control strategies proposed in this work are developed to control the active and reactive power of the DFIG on the one hand, and to maintain the DC-link voltage constant for the inverting function on the other hand. This is ensured by generating control signals for two power electronic converters, RSC (Rotor Side Converter) and GSC (Grid Side Converter). In order to optimise the power production in the WT (Wind Turbine), an MPPT (Maximum Power Point Tracking) algorithm is applied along with each control technique. To simulate the effectiveness of the proposed controllers, MATLAB/Simulink Software is used, and the obtained results are analysed and discussed to compare PI and backstepping controllers in terms of robustness against wind speed variations and tracking performance in dynamic and steady states.

Fractional-slot concentrated-winding permanent magnet synchronous machines (FSCW-PMSMs) have a good prospect of application in the drive system of electric and hybrid electric vehicles. However, the armature magnetomotive force (MMF) of FSCWPMSM contains a large number of space harmonics, which induce large magnet eddycurrent loss (ECL). To solve this problem, a dual three-phase 10-pole and 24-slot winding layout is proposed.MMFharmonic analysis shows that the 1st, 7th and 17th space-harmonic winding factors of the proposed winding can be reduced by 100%, 87% and 87% respectively, compared with a dual three-phase 10-pole and 12-slot winding. Electromagnetic performances of the proposed machine under rated sinusoidal current supply and space vector pulse-width-modulated (SVPWM) voltage supply are investigated based on 2D finite-element analysis. It is shown that the proposed machine can meet the requirement of torque and efficiency in the full speed range. Especially, magnet ECL can be reduced greatly due to the reduction of the 7th and 17th space harmonics.

This paper proposes two nonlinear exact and simple state space models of a Zsource converter (ZSC) connected to an ac grid. A generic model of a ZSC accompanied with proper controllers are proposed and a dynamic model of the whole system is derived; as a result, based on a simple one, an equivalent block diagram of the current-controlled ZSC system is proposed. The ac small signal stability method is applied and the impact of controller parameters on network’s stability is discussed. Besides, overall system dynamic performance has been assessed in the event of perturbations. Time-domain simulations have been implemented in PSCAD/EMTDC to validate the accuracy of the models and effectiveness of the proposed controllers. The results of the exact model are compared with the response of the equations which are applied in MATLAB.

The paper proposes a newrobust fuzzy gain adaptation of the sliding mode (SMC) power control strategy for the wind energy conversion system (WECS), based on a doubly fed induction generator (DFIG), to maximize the power extracted from the wind turbine (WT). The sliding mode controller can deal with any wind speed, ingrained nonlinearities in the system, external disturbances and model uncertainties, yet the chattering phenomenon that characterizes classical SMC can be destructive. This problem is suitably lessened by adopting adaptive fuzzy-SMC. For this proposed approach, the adaptive switching gains are adjusted by a supervisory fuzzy logic system, so the chattering impact is avoided. Moreover, the vector control of the DFIG as well as the presented one have been used to achieve the control of reactive and active power of the WECS to make the wind turbine adaptable to diverse constraints. Several numerical simulations are performed to assess the performance of the proposed control scheme. The results show robustness against parameter variations, excellent response characteristics with a reduced chattering phenomenon as compared with classical SMC.

One of the most important aims of the sizing and allocation of distributed generators (DGs) in power systems is to achieve the highest feasible efficiency and performance by using the least number of DGs. Considering the use of two DGs in comparison to a single DG significantly increases the degree of freedom in designing the power system. In this paper, the optimal placement and sizing of two DGs in the standard IEEE 33-bus network have been investigated with three objective functions which are the reduction of network losses, the improvement of voltage profiles, and cost reduction. In this way, by using the backward-forward load distribution, the load distribution is performed on the 33-bus network with the power summation method to obtain the total system losses and the average bus voltage. Then, using the iterative search algorithm and considering problem constraints, placement and sizing are done for two DGs to obtain all the possible answers and next, among these answers three answers are extracted as the best answers through three methods of fuzzy logic, the weighted sum, and the shortest distance from the origin. Also, using the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) and setting the algorithm parameters, thirty-six Pareto fronts are obtained and from each Pareto front, with the help of three methods of fuzzy logic, weighted sum, and the shortest distance from the origin, three answers are extracted as the best answers. Finally, the answer which shows the least difference among the responses of the iterative search algorithm is selected as the best answer. The simulation results verify the performance and efficiency of the proposed method.

This paper reports a new strand wire winding method in a solenoidal coil with limited geometry that enables good impedance matching. In the proposed method strand wires are wound layer-by-layer on top of each other allowing one to set equivalent inductance and resistance of the coil to desired values while obtaining dense magnetic flux and high current carrying capacity. As a proof-of-concept demonstration, simple model setups were constructed with solenoidal coils composed of copper wire strands wound according to the proposed method, and a plastic pipe. The measurements were repeated with a metal shell placed inside the coil to model a complete heating system. System inductance and resistance were measured at two different frequencies. The results show that with the new winding method it is possible to increase a coil’s turn number and the number of strand layers composed by the coil. Also, adding and removing strand layers in the proposed coil architectures enable inductance and resistance values to decrease and increase, respectively, in a controlled way. To understand changes of system parameters, simulations were also performed. The calculated inductance and resistance values in the simulations agree well with the measurement results and magnetic flux distribution created in the system demonstrates the changes.

In recent years, due to the increasing number of renewable energy sources, which are characterised by the stochastic nature of the generated power, interest in energy storage has increased. Commercial installations use simple deterministic methods with low economic efficiency. Hence, there is a need for intelligent algorithms that combine technical and economic aspects. Methods based on computational intelligence (CI) could be a solution. The paper presents an algorithm for optimising power flow in microgrids by using computational intelligence methods. This approach ensures technical and economic efficiency by combining multiple aspects in a single objective function with minimal numerical complexity. It is scalable to any industrial or residential microgrid system. The method uses load and generation forecasts at any time horizon and resolution and the actual specifications of the energy storage systems, ensuring that technological constraints are maintained. The paper presents selected calculation results for a typical residential microgrid supplied with a photovoltaic system. The results of the proposed algorithm are compared with the outcomes provided by a deterministic management system. The computational intelligence method allows the objective function to be adjusted to find the optimal balance of economic and technical effects. Initially, the authors tested the invented algorithm for technical effects, minimising the power exchanged with the distribution system. The application of the algorithm resulted in financial losses, €12.78 for the deterministic algorithm and €8.68 for the algorithm using computational intelligence. Thus, in the next step, a control favouring economic goals was checked using the CI algorithm. The case where charging the storage system from the grid was disabled resulted in a financial benefit of €10.02, whereas when the storage system was allowed to charge from the grid, €437.69. Despite the financial benefits, the application of the algorithm resulted in up to 1560 discharge cycles. Thus, a new unconventional case was considered in which technical and economic objectives were combined, leading to an optimum benefit of €255.17 with 560 discharge cycles per year. Further research of the algorithm will focus on the development of a fitness function coupled to the power system model.

Modern drives with Permanent Magnet Synchronous Motors (PMSMs) require both efficient control structure to ensure excellent dynamics and effective diagnostic algorithms to detect the motor faults that can occur. This paper shows the combination of both mentioned aspects – the direct-axis based signals of the Field Oriented Control (FOC) structure are proposed as diagnostic signals to allow diagnosing the interturn short-circuit failure that can appear inside stator windings. The amplitudes of second order harmonics are selected as the fault indicators. Different modelling methods are analysed and compared in detail in this paper: an analytical mathematical model, a Finite Element Method (FEM)- based model and next verified using a laboratory setup. The results obtained using all the mentioned models proved that the proposed fault indices are increasing significantly with the number of shorted turns and are independent on the load torque level.