New design and comparative study via two techniques for wind energy conversion system

Introduction. With the advancements in the variable speed direct drive design and control of wind energy systems, the efficiency and energy capture of these systems is also increasing. As such, numerous linear controllers have also been developed, in literature, for MPPT which use the linear characteristics of the wind turbine system. The major limitation in all of those linear controllers is that they use the linearized model and they cannot deal with the nonlinear dynamics of a system. However, real systems exhibit nonlinear dynamics and a nonlinear controller is required to handle such nonlinearities in real-world systems. The novelty of the proposed work consists in the development of a robust nonlinear controller to ensure maximum power point tracking by handling nonlinearities of a system and making it robust against changing environmental conditions. Purpose. In the beginning, sliding mode control has been considered as one of the most powerful control techniques, this is due to the simplicity of its implementation and robustness compared to uncertainties of the system and external disturbances. Unfortunately, this type of controller suffers from a major disadvantage, that is, the phenomenon of chattering. Methods. So in this paper and in order to eliminate this phenomenon, a novel non-linear control algorithm based on a synergetic controller is proposed. The objective of this control is to maximize the power extraction of a variable speed wind energy conversion system compared to sliding mode control by eliminating the phenomenon of chattering and have a good power quality by fixing the power coefficient at its maximum value and the Tip Speed Ratio maintained at its optimum value. Results. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink environment. The simulation results show the effectiveness of the proposed scheme, suppression of the chattering phenomenon and robustness of the proposed controller compared to the sliding mode control law.Вступ. З досягненнями у проектуванні та керуванні вітряними енергосистемами з регульованою швидкістю, зростають також ефективність та захоплення енергії цих систем. Так, в літературі також розроблено численні лінійні контролери для відстеження точки максимальної потужності, які використовують лінійні характеристики системи з вітряними турбінами. Основним обмеженням у всіх цих лінійних контролерах є те, що вони використовують лінеаризовану модель і не можуть мати справу з нелінійною динамікою системи. Однак реальні системи демонструють нелінійну динаміку, і для обробки таких нелінійностей у реальних системах необхідний нелінійний контролер. Новизна запропонованої роботи полягає у розробці надійного нелінійного контролера для забезпечення відстеження точки максимальної потужності шляхом обробки нелінійності системи та забезпечення її стійкості до змін умов навколишнього середовища. Мета. Спочатку управління ковзним режимом вважалося одним з найпотужніших методів управління, що пов’язано з простотою його реалізації та надійністю порівняно з невизначеністю системи та зовнішніми збуреннями. На жаль, цей тип контролера страждає від головного недоліку, а саме явища вібрування. Методи. Тому у цій роботі з метою усунення цього явища пропонується новий нелінійний алгоритм управління, заснований на синергетичному контролері. Завдання цього контролю – максимізувати відбір потужності системи перетворення енергії вітру зі змінною швидкістю порівняно із регулюванням ковзного режиму, усуваючи явище вібрування, і мати хорошу якість енергії, фіксуючи коефіцієнт потужності на його максимальному значенні та підтримуючи кінцевий коефіцієнт швидкості на його оптимальному значенні. Результати. Ефективність запропонованих нелінійних контролерів перевірена в середовищі MATLAB/Simulink. Результати моделювання показують ефективність запропонованої схеми, придушення явища вібрування та стійкість запропонованого контролера порівняно із законом управління ковзного режиму

Energy management based on a fuzzy controller of a photovoltaic/fuel cell/Li-ion battery/supercapacitor for unpredictable, fluctuating, high-dynamic three-phase AC load

Introduction. Nowadays, environmental pollution becomes an urgent issue that undoubtedly influences the health of humans and other creatures living in the world. The growth of hydrogen energy increased 97.3 % and was forecast to remain the world’s largest source of green energy. It can be seen that hydrogen is one of the essential elements in the energy structure as well as has great potential to be widely used in the 21st century. Purpose. This paper aims to propose an energy management strategy based a fuzzy logic control, which includes a hybrid renewable energy sources system dedicated to the power supply of a three-phase AC variable load (unpredictable high dynamic). Photovoltaic (PV), fuel cell (FC), Li-ion battery, and supercapacitor (SC) are the four sources that make up the renewable hybrid power system; all these sources are coupled in the DC-link bus. Unlike usual the SC was connected to the DC-link bus directly in this research work in order to ensure the dominant advantage which is a speedy response during load fast change and loads transient. Novelty. The power sources (PV/FC/Battery/SC) are coordinated based on their dynamics in order to keep the DC voltage around its reference. Among the main goals achieved by the fuzzy control strategy in this work are to reduce hydrogen consumption and increase battery lifetime. Methods. This is done by controlling the FC current and by state of charge (SOC) of the battery and SC. To verify the fuzzy control strategy, the simulation was carried out with the same system and compared with the management flowchart strategy. The results obtained confirmed that the hydrogen consumption decreased to 26.5 g and the SOC for the battery was around 62.2-65 and this proves the desired goal.Вступ. В даний час забруднення навколишнього середовища стає актуальною проблемою, яка, безперечно, впливає на здоров’я людини та інших істот, які живуть у світі. Зростання водневої енергетики збільшилося на 97,3 %, і прогнозувалося, що вона залишиться найбільшим у світі джерелом зеленої енергії. Видно, що водень є одним із найважливіших елементів у структурі енергетики, а також має великий потенціал для широкого використання у 21 столітті. Мета. У цій статті пропонується стратегія управління енергоспоживанням, заснована на нечіткому логічному управлінні, яка включає гібридну систему відновлюваних джерел енергії, призначену для живлення трифазного змінного навантаження змінного струму (непередбачувана висока динаміка). Фотоелектричні (PV), паливні елементи (FC), літій-іонні батареї та суперконденсатори (SC) – це чотири джерела, з яких складається відновлювана гібридна енергосистема; всі ці джерела підключені до шини постійного струму. На відміну від звичайних застосувань,ув цій дослідницькій роботі SC був підключений до шини постійного струму безпосередньо, щоб забезпечити домінуючу перевагу, що полягає в швидкому реагуванні при швидкій зміні навантаження та перехідних режимах навантаження. Новизна. Джерела живлення (PV/FC/батареї/SC) координуються на основі їхньої динаміки, щоб підтримувати напругу постійного струму біля свого еталонного значення. Серед основних цілей, досягнутих стратегією нечіткого управління у цій роботі, - зниження споживання водню та збільшення терміну служби батареї. Методи. Це робиться шляхом керування струмом FC та станом заряду (SOC) батареї та SC. Для перевірки стратегії нечіткого управління було проведено моделювання з тією самою системою та порівняння зі стратегією блок-схеми керування. Отримані результати підтвердили, що споживання водню знизилося до 26,5 г, а SOC для батареї становило близько 62,2-65, що доводить досягнення бажаної мети

Modelling and diagnostic of an ultrasonic piezoelectric actuator

Modeling of piezoelectric motors is a difficult task because their characteristics are affected by various factors such as materials properties, electrical and mechanical boundary conditions. This work presents the modeling of piezoelectric motor via bond graph method and used for the diagnostic. This method is an innovative way to analyse the effects of different design variables on the objective function but can be also considered as an optimization stage of the study. The validation and the development of bond graph models are based on physical insight to aid in structural damage detection and use the technique of optimal sensors placement

Implementation of MPPT Algorithm and Supervision of Shading on Photovoltaic Module

This paper presents an implementation of MPPT (maximum power point tracking) algorithm based on real-time measurements and on model-based simulation. For the supervision of a photovoltaic module, different cases of shading are used. Attention was given on advanced control for the supervision of a photovoltaic system according to the need to maximize energy output. Experimental results verified the performance and the feasibility of the proposed system

Techno-Economic Feasibility Study of Investigation of Renewable Energy System for Rural Electrification in South Algeria

This work aims to consider the combination of different technologies regarding energy production and management with four possible configurations. We present an energy management algorithm to detect the best design and the best configuration from the combination of different sources. This combination allows us to produce the necessary electrical energy for supplying habitation without interruption. A comparative study is conducted among the different combinations on the basis of the cost of energy, diesel consumption, diesel price, capital cost, replacement cost, operation, and maintenance cost and greenhouse gas emission. Sensitivity analysis is also performed

Attachment, Neurobiology, and Mentalizing along the Psychosis Continuum.

In this review article, we outline the evidence linking attachment adversity to psychosis, from the premorbid stages of the disorder to its clinical forms. To better understand the neurobiological mechanisms through which insecure attachment may contribute to psychosis, we identify at least five neurobiological pathways linking attachment to risk for developing psychosis. Besides its well documented influence on the hypothalamic-pituary-adrenal (HPA) axis, insecure attachment may also contribute to neurodevelopmental risk through the dopaminergic and oxytonergic systems, as well as bear influence on neuroinflammation and oxidative stress responses. We further consider the neuroscientific and behavioral studies that underpin mentalization as a suite of processes potentially moderating the risk to transition to psychotic disorders. In particular, mentalization may help the individual compensate for endophenotypical impairments in the integration of sensory and metacognitive information. We propose a model where embodied mentalization would lie at the core of a protective, resilience response mitigating the adverse and potentially pathological influence of the neurodevelopmental cascade of risk for psychosis

Changes of motor corticobulbar projections following different lesion types affecting the central nervous system in adult macaque monkeys.

Functional recovery from central nervous system injury is likely to be partly due to a rearrangement of neural circuits. In this context, the corticobulbar (corticoreticular) motor projections onto different nuclei of the ponto-medullary reticular formation (PMRF) were investigated in 13 adult macaque monkeys after either, primary motor cortex injury (MCI) in the hand area, or spinal cord injury (SCI) or Parkinson's disease-like lesions of the nigro-striatal dopaminergic system (PD). A subgroup of animals in both MCI and SCI groups was treated with neurite growth promoting anti-Nogo-A antibodies, whereas all PD animals were treated with autologous neural cell ecosystems (ANCE). The anterograde tracer BDA was injected either in the premotor cortex (PM) or in the primary motor cortex (M1) to label and quantify corticobulbar axonal boutons terminaux and en passant in PMRF. As compared to intact animals, after MCI the density of corticobulbar projections from PM was strongly reduced but maintained their laterality dominance (ipsilateral), both in the presence or absence of anti-Nogo-A antibody treatment. In contrast, the density of corticobulbar projections from M1 was increased following opposite hemi-section of the cervical cord (at C7 level) and anti-Nogo-A antibody treatment, with maintenance of contralateral laterality bias. In PD monkeys, the density of corticobulbar projections from PM was strongly reduced, as well as that from M1, but to a lesser extent. In conclusion, the densities of corticobulbar projections from PM or M1 were affected in a variable manner, depending on the type of lesion/pathology and the treatment aimed to enhance functional recovery

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Distortions of perceived volume and length of body parts

We experience our body as a 3D, volumetric object in the world. Measures of our conscious body image, in contrast, have investigated the perception of body size along one or two dimensions at a time. There is, thus, a discrepancy between existing methods for measuring body image and our subjective experience of having 3D body. Here we assessed in a sample of healthy adults the perception of body size in terms of its 1D length and 3D volume. Participants were randomly assigned to two groups using different measuring units (other body part and non-body object). They estimated how many units would fit in a perceived size of body segments and the whole body. The patterns of length and volume misperception across judged segments were determined as their perceived size proportional to their actual size. The pattern of volume misperception paints the representation of 3D body proportions resembling those of a somatosensory homunculus. The body parts with a smaller actual surface area relative to their volume were underestimated more. There was a tendency for body parts underestimated in volume to be overestimated in length. Perceived body proportions thus changed as a function of judgement type while showing a similarity in magnitude of the absolute estimation error, be it an underestimation of volume or overestimation of length. The main contribution of this study is assessing the body image as a 3D body representation, and thus extending beyond the conventional ‘allocentric’ focus to include the body on the inside. Our findings highlight the value of studying the perceptual distortions “at the baseline”, i.e. in healthy population, so as to advance the understanding of the nature of perceptual distortions in clinical conditions