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The future smart grids will contain a high number of Plug-in Electric Vehicles (PEVs) that will move in the grid widely. The high penetration of these devices will bring new challenges regarding the optimal operation and management of the system. In this way, this paper proposes a realistic framework to first model PEVs movements in the grid and second schedule their movement for minimizing the costs. The cost function consists of the total network cost for supplying the electric loads and PEVs for 24 hour time horizon. According to the high complexities of the problem, a new optimization framework based on teacher learning algorithm (TLO) with a new modification method is proposed to search the problem space thoroughly. The feasibility and satisfying performance of the proposed optimization framework are examined on the IEEE test system. [ABSTRACT FROM AUTHOR]

The idea of smart grids has created new opportunities in the electrical networks for monitoring the system status. One of the valuable and significant techniques for monitoring the smart grids is state estimation. In this way, this paper proposes a sufficient state estimation algorithm for inclusive monitoring of the distribution systems in the presence of RESs (renewable energy sources). The proposed method is a hybrid technique using WLS (weighted least square) method and FA (firefly algorithm) to reach more reliable and accurate state estimation of the network. FA is equipped with new optimization operators that make it possible to solve the multi-modal problems using an automatic sub-division feature. In order to improve the overall search ability of the algorithm, a new two-phase modification method is proposed. The proposed hybrid method can estimate the voltage angle using the WLS method. The simulation results show more optimal cost function value with faster response with escaping from the several local optima of the problem. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy PI controller as a power system stabilizer. The fuzzy PI controller has been designed in order to prepare an auxiliary signal for the excitation system of the synchronous generator. The proposed controller utilizes a combination of a fuzzy logic controller (FLC) and a PI controller, which are the most recent researches in the problem at hand. In comparison with the conventional fuzzy controllers, the proposed fuzzy PI controller takes advantage of both FLC and conventional PI controller to overcome all kind of disturbances. The damping of oscillations local modes in a power system will substantially be controlled by employing this controller as a replacement for conventional PSS. Furthermore, the presented paper considers the conventional PSS and compares its performance with respect to three proposed types of fuzzy PI controller. Correspondingly, the effects of the supplementary signals in damping oscillation have been shown. Finally, to scrutinize the effectiveness of the proposed controller in damping oscillations, a three-phase short circuit condition is studied in order to clarify the application of the developed methodology. The achieved outcomes show that the proposed controller for stabilizing power system can provide very good damping characteristic. [ABSTRACT FROM AUTHOR]

In the smart grids area, attractive issues are created for the hackers such that they tend to interface the control of smart grids deliberately or unintentionally so that the normal operations of these systems are endangered. According to the insight of the smart grids systems, it is anticipated that in near future, all the distribution systems would be operated smartly. In this regard, the state estimation and control algorithms should be improved to be able to detect and omit fake data of measurements appropriately. Some methods for state estimation, such as Kalman filter as a proper criterion can omit the noisy data from the measurements. Similarly, the chi‐square method is capable of detecting bad data, but cannot detect and remove unreal and fake data. The purpose of this work is to propose a suitable method for state estimation in the smart grids considering the possibility of having fake data measurement. It will be shown that the use of evolutionary algorithms, such as bat algorithm with a hybrid approach based on the weighted least square technique makes it possible to detect and omit fake data. [ABSTRACT FROM AUTHOR]

Distribution feeder reconfiguration (DFR) is formulated as a multi-objective optimization problem which minimizes real power losses, deviation of the node voltages and the number of switching operations and also balances the loads on the feeders. In the proposed method, the distance ( λ2 norm) between the vector-valued objective function and the worst-case vector-valued objective function in the feasible set is maximized. In the algorithm, the status of tie and sectionalizing switches are considered as the control variables. The proposed DFR problem is a non-differentiable optimization problem. Therefore, a new hybrid evolutionary algorithm based on combination of fuzzy adaptive particle swarm optimization (FAPSO) and ant colony optimization (ACO), called HFAPSO, is proposed to solve it. The performance of HFAPSO is evaluated and compared with other methods such as genetic algorithm (GA), ACO, the original PSO, Hybrid PSO and ACO (HPSO) considering different distribution test systems. [ABSTRACT FROM AUTHOR]

We present an algorithm of supervisory control with fuzzy logic and adaptive control of a low earth orbit (LEO) satellite with single-spin stabilization. The error generated by an adaptive control signal can be computed with only the reference dynamics of the model and the damping. A fuzzy logic algorithm is presented that acts as a supervisory control and estimates the changes of the parameters and applies the necessary signals to avoid any unsuitability in the system. The results of simulation have shown that position control is performed within an acceptable tolerance by using the suggested algorithm, which includes three closed-loop systems and unknown parameters of the system [1,2,3]. [ABSTRACT FROM AUTHOR]

Infections related to biomedical devices and materials remain a critical healthcare concern, leading to considerable morbidity and disease burden. To combat this issue, the development of fibers with inherent antibacterial properties has garnered significant interest. These fibers offer a large surface area, making them ideal candidates for effective treatment with antibacterial agents. In this study, we present a straightforward procedure for applying silver nanoparticles (AgNPs) to hydrophobic polylactic acid (PLA) fibers that have been surface-modified using sodium hydroxide (NaOH). The method involved electrospinning to create a high-thickness web of PLA fibers, followed by gradual surface modification with NaOH at two different concentrations (0.5 M and 1 M). To confer antibacterial properties to the modified surface samples, we applied silver nanoparticles (AgNPs) at a concentration of 25 mM. In contrast to regular PLA fibers, the surfaces of hydrolyzed PLA (PLA-H) fibers, AgNPs-treated PLA (PLA-A), and hydrolyzed-AgNPs coated PLA (PLA-H-A) fibers exhibited a non-uniform and highly porous structure. EDX analysis provided crucial information about the presence and integration of Ag nanoparticles into the PLA fibers. The PLA-H-A sample exhibited the highest hydrophilicity, with a contact angle of 65.7°. Additionally, the results from differential scanning calorimetry indicated an increase in PLA-H-A's glass transition temperature. Notably, in the Gram-positive Escherichia coli (E. coli) and Gram-negative Staphylococcus aureus (S. aureus) tests, the PLA-H-A samples demonstrated highly effective antibacterial properties, effectively preventing bacterial growth. [ABSTRACT FROM AUTHOR]

Background Despite their potential for improving health outcomes, mobile-based home monitoring systems for heart failure have not yet been taken up widely by the patients and providers. Objectives To design and iteratively move towards a personalised mobile health monitoring system for patients living with heart failure, according to their health care and usability needs. Methods We present an iterative approach to refining a remote health monitoring system that is based on interactions between different actors (patients, clinicians, social scientists and engineers) and supports the collection of quantitative and qualitative information about user experience and engagement. Patients were provided with tablet computers and commercially available sensing devices (a blood pressure monitor, a set of weighing scales, and a pulse oximeter) in order to complete physiological measurements at home, answer symptom-specific questionnaires, review their personal readings, view educational material on heart failure self-management, and communicate with their health professionals. The system supported unobtrusive remote software upgrades via an application distribution channel and the activation or deactivation of functional components by health professionals during run-time operation. We report early findings from the application of this approach in a cohort of 26 heart failure patients (mean age 72 ± 15 years), their caregivers and healthcare professionals who participated in the SUPPORT-HF (Seamless User-centred Proactive Provision Of Risk-stratified Treatment for Heart Failure) study over a one-year study period (mean patient follow-up duration = 270 ± 62 days). Results The approach employed in this study led to several system upgrades dealing in particular with patient requirements for better communication with the development team and personalised self-monitoring interfaces. Engagement with the system was constantly high throughout the study and during the last week of the evaluation, 23 patients (88%) used the system at least once and 16 patients (62%) at least three times. Conclusions Designers of future mobile-based home monitoring systems for heart failure and other chronic conditions could leverage the described approach as a means of meeting patients’ needs during system use within the home environment and facilitating successful uptake. [ABSTRACT FROM AUTHOR]

Summary There is increasing interest in how the phase of local oscillatory activity within a brain area determines the long-range functional connectivity of that area. For example, increasing convergent evidence from a range of methodologies suggests that beta (20 Hz) oscillations may play a vital role in the function of the motor system [ 1–5 ]. The “communication through coherence” hypothesis posits that the precise phase of coherent oscillations in network nodes is a determinant of successful communication between them [ 6, 7 ]. Here we set out to determine whether oscillatory activity in the beta band serves to support this theory within the cortical motor network in vivo. We combined non-invasive transcranial alternating-current stimulation (tACS) [ 8–12 ] with resting-state functional MRI (fMRI) [ 13 ] to follow both changes in local activity and long-range connectivity, determined by inter-areal blood-oxygen-level-dependent (BOLD) signal correlation, as a proxy for communication in the human cortex. Twelve healthy subjects participated in three fMRI scans with 20 Hz, 5 Hz, or sham tACS applied separately on each scan. Transcranial magnetic stimulation (TMS) at beta frequency has previously been shown to increase local activity in the beta band [ 14 ] and to modulate long-range connectivity within the default mode network [ 15 ]. We demonstrated that beta-frequency tACS significantly changed the connectivity pattern of the stimulated primary motor cortex (M1), without changing overall local activity or network connectivity. This finding is supported by a simple phase-precession model, which demonstrates the plausibility of the results and provides emergent predictions that are consistent with our empirical findings. These findings therefore inform our understanding of how local oscillatory activity may underpin network connectivity. [ABSTRACT FROM AUTHOR]