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The wind turbine (WT) is a complex system subjected to wind disturbances. Because the aerodynamics is nonlinear, the control is thus challenging. For the variation of wind speed when rated power is delivered at rated wind speed, the power is limited to the rate by the pitching of the blades of the turbine. This paper aims to address pitch control with the WT benchmark model. The possible use of appropriate adaptive controller design that modifies the control action automatically identifying any change in system parameters is explored.

To deal with pitch control problem when wind speed exceeds the rated wind speed of the WT, six digital self-tuning controller (STC) with different structures such as proportional integral (PI), proportional derivative (PD), Dahlin’s, pole placement, deadbeat and Takahashi has been taken herein. The system model is identified as a second-order autoregressive exogenous (ARX) model by three techniques for comparison: recursive least square method (RLS), RLS with exponential forgetting and RLS with adaptive directional forgetting identification methods. A comparative study of three identification methods, six adaptive controllers with the conventional PI controller and sliding mode controller (SMC), are shown.

As per the results, the best improvement in control of the output power by pitching in full load region of benchmark model is achieved by self-tuning PD controller based on RLS with adaptive directional forgetting method. The adaptive control design has a future in WT control applications.

A comparative study of identification methods, six adaptive controllers with the conventional PI controller and SMC, are shown here. As per the results, the best improvement in control of the output power by pitching in the full load region of the benchmark model has been achieved by self-tuning PD controller. The best identification method or the system is RLS with an adaptive directional forgetting method. Instead of a step input response design for the controllers, the controller design has been carried out for the stochastic wind and the performance is adjudged by the normalized sum of square tracking error (NSSE) index. The validation of the proposed self-tuning PD controller has been shown in comparison to the conventional controller with Monte-Carlo analysis to handle model parameter alteration and erroneous measurement issues.

The purpose of this study is to improve the control performance of wind energy conversion systems (WECSs) by proposing a new sensorless, robust control strategy based on a Smith predictor active disturbance rejection control (SP-ADRC) associated with a speed/position estimator.

The estimator consists of a sliding mode observer (SMO) in combination with a phase-locked loop (PLL) to estimate the permanent magnet synchronous generator (PMSG) rotor position and speed. At the same time, the SP-ADRC is applied to the speed control loop of the variable-speed WECS control system to adapt strongly to dynamic characteristics under parameter uncertainties and disturbances.

Numerical simulations are conducted to evaluate the speed tracking performances under various wind speed profiles. The results show that the proposed sensorless speed control improves the accuracy of rotor speed and position estimation and provides better power tracking performance than a regular ADRC controller under fast wind speed variations.

This paper offers a new approach for designing sensorless, robust control for PMSG-based WECSs.

A new sensorless, robust control is proposed to improve the stability and tracking performance of PMSG-based WECSs. The SP-ADRC control attenuates the effects of parameter uncertainties and disturbances and eliminates the time-delay impact. The sensorless control design based on SMO and PLL improves the accuracy of rotor speed estimation and reduces the chattering problem of traditional SMO. The obtained results support the theoretical findings.

The purpose of this paper is to modernize the generator system of wind turbine concept that not only improves the efficiency and power density but also reduces the system cost making design simpler and less expensive, especially in large-scale production.

This paper presents a new permanent magnet transverse flux generator (PMTFG) for wind energy production. The key feature of its composition is the double armature coil in a semi-closed stator core. The main structural difference of the presented design is the use of double coil in the same space of semi-closed stator core and reduced number of stator pole pairs and rotor magnets from 12/24 to 10/20. 3D simulations are performed using finite element analysis (FEA) to measure induced voltage and magnetic field distribution at no load. The FEA is performed to quantify the change in flux linkage, induced voltage and output power as a function of different speeds and load current.

Results show that PMTFG with double coil configuration has improved electromagnetic performance in terms of flux linkage, induced voltage, output power and efficiency. The power density of 10/20 PMTFG with the double coil is 0.0524 KW/Kg, about an 18% increase compared to the conventional design.

The proposed PMTFG is highly recommended for direct drive applications such as wind power.

Four models are simulated by FEA with single and double coil configuration, and load analysis is performed on all simulated models. Finally, results are compared with conventional PMTFG.

Currently, more renewable energy resources with advanced technology levels are incorporated in the electric power networks. Under this circumstance, the attainment of optimal economic dispatch is very much essential by the power system as the system requires more power generation cost and also has a great demand for electrical energy. Therefore, one of the primary difficulties in the power system is lowering the cost of power generation, which includes both economic and environmental costs. This study/paper aims to introduce a meta-heuristic algorithm, which offers an solution to the combined economic and emission dispatch (CEED).

A novel algorithm termed Levy-based glowworm swarm optimization (LGSO) is proposed in this work, and it provides an excellent solution to the combined economic and emission dispatch (CEED) difficulties by specifying the generation of the optimal renewable energy systems (RES). Moreover, in hybrid renewable energy systems, the proposed scheme is extended by connecting the wind turbine because the thermal power plant could not control the aforementioned costs. In terms of economic cost, emission cost and transmission loss, the suggested CEED model outperforms other conventional schemes genetic algorithm, Grey wolf optimization, whale optimization algorithm (WOA), dragonfly algorithm (DA) and glowworm swarm optimization (GSO) and demonstrates its efficiency.

According to the results, the suggested model for Iteration 20 was outperformed GSO, DA and WOA by 23.46%, 97.33% and 93.33%, respectively. For Iteration 40, the proposed LGSO was 60%, 99.73% and 97.06% better than GSO, DA and WOA methods, respectively. The proposed model for Iteration 60 was 71.50% better than GSO, 96.56% better than DA and 95.25% better than WOA. As a result, the proposed LGSO was shown to be superior to other existing techniques with respect to the least cost and loss.

This research introduces the latest optimization algorithm known as LGSO to provide an excellent solution to the CEED difficulties by specifying the generation of the optimal RES. To the best of the authors’ knowledge, this is the first work that utilizes LGSO-based optimization for providing an excellent solution to the CEED difficulties by specifying the generation of the optimal RES.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

Because of the shortage of energy, the development of green and reliable energy is particularly important. As a green and clean energy, wind power is widely used. As the core component of wind power generation, it is particularly important to choose generators with high reliability. Switched reluctance machine is widely used as generators because of its strong fault tolerance and high reliability. Therefore, this paper aims to propose a power converter and its control strategy to improve the efficiency of switched reluctance generators.

In this paper, a full-bridge power converter (FBPC) instead of the asymmetric half-bridge power converter (AHBPC) is adopted to drive the switched reluctance generator (SRG) system. Compare the FBPC with the AHBPC, the FBPC has several advantages including low cost and modularization, and operation process of SRG winding current direction is variable.

The results show that the SRG system can keep smooth operation by the FBPC with relatively high efficiency.

The FBPC is suitable to drive the SRG system. Meanwhile, this paper introduces two excitation modes of the FBPC as three-phase three-beat mode and six-phase six-beat mode. When the six-phase six-beat control strategy is adopted, the dead band time of the converter can be avoided. At the same time, the SRG has higher efficiency.

Superconductors offer several advantages compared with conventional conductors. However, it is not clear at this stage whether these types of conductors provide the same durability. For this reason, tape conductors under mechanical forces need to be studied in detail. The purpose of this paper is to investigate the relationship between critical temperature and axial mechanical stress of GdBaCuO tape conductors.

The paper investigates the influence of axial mechanical stresses on the critical temperature of superconductors. For these investigations, a multi-physical test rig was developed, which makes it possible to perform these types of investigations. With the presented measurement methodology, the influence of mechanical stresses on the tape conductor can be determined.

The investigations show a correlation between the critical temperature and the acting mechanical stresses. The analytically presented approach to describe the transition temperature is valid for the investigated samples. In addition, it is determined that the effects are not reversible, and therefore, permanent damage to the tape conductor is observed.

The presented investigations make it possible to create more accurate models of GdBaCuO tape conductors. This enables to extend the superconducting state space, which so far depends on three critical quantities, by the quantity of the axial stress.

This study aims to define a “technological statecraft” concept to distinguish tech-based measures/sanctions from an array of economic measures ranging from restrictions of rare earth elements and natural gas supplies to asset freezes under the wider portfolio of economic statecraft. This concept is practically intended to reveal the USA’s “logic of choice” in its employment of technology as an efficient instrument to deal with China in the context of the great power rivalry.

This study follows David A. Baldwin’s statecraft definition and conceptualization methodology, which relies on “means” rather than “ends.” In addition to Baldwin and as an incremental contribution to his economic statecraft analysis, this study also combines national political economy with statecraft analysis with a particular focus on the utilization of technological measures against China during the Trump administration.

The US rationale for choosing technology, namely, emerging and foundational technologies, in its rivalry against China is caused at least by two factors: the nature of the external challenge and the characteristics of the US innovation model based largely on radical innovations. To deal with China, the USA practically distinguished the role of advanced technology and followed a grammer of technological statecraft as depicted in the promulgated legal texts during the Trump administration.

Despite a growing volume of literature on economic statecraft and technological competition, studies focusing on countries’ “logic of choice” with regard to why and under what conditions they choose financial, technological or commodity-based sanctions/measures/controls are lacking. Inspired from Baldwin’s account on the “logic of choice” from among alternative statecrafts (i.e. diplomacy, military, economic statecraft, and propaganda). This study will contribute to the literature with a clear lens to demonstrate the “logic of choice” from among a variety of economic statecraft measures in the case of the US technological statecraft toward China.

This paper aims to present the project of a permanent magnet synchronous machine which can be used as generator in the vertical axis wind turbine.

In the study, finite element analysis was used to perform simulation research of electrical machines. Based on the simulation studies, an experimental model was built. The paper presents also selected experimental results.

During the research, it was found that the radial arrangement of the permanent magnets is more favorable than the tangential one for the selected structure of the generator with permanent magnets.

During the experimental research, a problem was encountered involving the correct control of the constructed generator at low rotational speeds.

The proposed solution can be used in low-speed vertical axis wind turbines.

The presented research fits the global trend toward the use of alternative and renewable energy sources.

The paper presents new simulation studies of two low-speed generator topologies. The results for the radial and tangential arrangement of the permanent magnets in the rotor were verified. Based on this research, an experimental prototype of a generator for a slow-speed vertical axis wind turbine was built.