Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database

Influence of Non-Linearity in Losses Estimation of Magnetic Components for DC-DC Converters

In this paper, the problem of estimating the core losses for inductive components is addressed. A novel methodology is applied to estimate the core losses of an inductor in a DC-DC converter in the time-domain. The methodology addresses both the non-linearity and dynamic behavior of the core magnetic material and the non-uniformity of the field distribution for the device geometry. The methodology is natively implemented using the LTSpice simulation environment and can be used to include an accurate behavioral model of the magnetic devices in a more complex lumped circuit. The methodology is compared against classic estimation techniques such as Steinmetz Equation and the improved Generalized Steinmetz Equation. The validation is performed on a practical DC-DC Buck converter, which was utilized to experimentally verify the results derived by a model suitable to estimate the inductor losses. Both simulation and experimental test confirm the accuracy of the proposed methodology. Thus, the proposed technique can be flexibly used both for direct core loss estimation and the realization of a subsystem able to simulate the realistic behavior of an inductor within a more complex lumped circuit

CFSO3: A New Supervised Swarm-Based Optimization Algorithm

We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3the tuning of parameters isa prioridesigned for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3together with validations on classical benchmarks are presented

Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database

On Training Efficiency and Computational Costs of a Feed Forward Neural Network: A Review

A comprehensive review on the problem of choosing a suitable activation function for the hidden layer of a feed forward neural network has been widely investigated. Since the nonlinear component of a neural network is the main contributor to the network mapping capabilities, the different choices that may lead to enhanced performances, in terms of training, generalization, or computational costs, are analyzed, both in general-purpose and in embedded computing environments. Finally, a strategy to convert a network configuration between different activation functions without altering the network mapping capabilities will be presented

High-energy emulsification of Allium sativum essential oil boosts insecticidal activity against Planococcus citri with no risk to honeybees

The ecotoxicological consequences of synthetic pesticides have encouraged stakeholders to search for eco-friendly pest control tools, like essential oils (EOs). Nano-delivery systems (nanoparticles and nano-emulsions) seem ideal for developing EO-based biopesticides, although production processes should be standardized and implemented. In this study, nano-emulsions loaded with a high amount of Allium sativum L. EO (15%) were developed using different mixed bottom-up/top-down processes. Garlic EO was chemically analyzed by gas chromatography-mass spectrometry (GC-MS) and formulations were physically characterized using Dynamic Light Scattering (DLS) apparatus. The insecticidal activity against Planococcus citri Risso (Hemiptera: Pseudococcidae) and selectivity toward Apis mellifera L. (Hymenoptera: Apidae) worker bees was evaluated. Garlic EO was mainly composed of sulphur components (96.3%), with diallyl disulphide and diallyl trisulphide as the most abundant compounds (37.26% and 28.15%, respectively). Top-down processes could produce stable nano-emulsions with droplet size in the nanometric range (< 200nm) and good polydispersity index (PDI < 0.2). In contrast, the bottom-up emulsion was unstable, and its droplet size was around 500nm after 24 hours. High-energy emulsification processes significantly increased the residual toxicity of garlic EO against 3rd instar P. citri nymphs, whereas the developed formulations were harmless to A. mellifera workers in topical application. This study confirmed that the production process significantly affected the physical properties and efficacy against target pests. The lack of adverse impact on honeybees denotated the potential of these formulations as bioinsecticides in organic and/or IPM programs, although further extended ecotoxicological studies are necessary

Age differences in anticipatory and executory mechanisms of gait initiation following unexpected balance perturbations

Purpose. An age-related decline in anticipatory postural mechanisms has been reported during gait initiation; however, it is unclear whether such decline may jeopardize whole-body stability following unexpected balance perturbations. This study aimed to compare young and older individuals’ ability to generate postural responses and preserve stability in response to external waist perturbations delivered within gait initiation. Methods. Ten young and ten older participants performed 10 gait initiation trials followed by 48 unperturbed and 12 perturbed trials in a random order. A stereophotogrammetric system and three force platforms were used to quantify mechanical parameters from the preparatory phase (e.g., timing and amplitude of postural adjustments) and from the stepping phase (e.g., step characteristics and dynamic stability). Activation patterns of lower leg muscles were determined by surface electromyography. Results. Older participants responded to perturbation with lower increase in both magnitude (p<0.001; η 2 p=0.62) and duration of (p=0.001; η 2 p=0.39) preparatory parameters and soleus muscle activity (p<0.001; η 2 p=0.55), causing shorter (p<0.001; η 2 p=0.59) and lower (p<0.001; η 2 p=0.43) stepping, compared to young participants. Interestingly, young participants showed greater correlations between preparatory phase parameters and dynamic stability of the first step than older participants (average r of -0.40 and -0.06, respectively). Conclusion. The results suggest that young participants took more time than older to adjust the anticipatory biomechanical response to perturbation attempting to preserve balance during stepping. In contrast, older adults were unable to modify their anticipatory adjustments in response to perturbation and mainly relied on compensatory mechanisms attempting to preserve stability via a more cautious stepping strategy

Effects of Functional Electrical Stimulation Cycling of Different Duration on Viscoelastic and Electromyographic Properties of the Knee in Patients with Spinal Cord Injury

The benefits of functional electrical stimulation during cycling (FES-cycling) have been ascertained following spinal cord injury. The instrumented pendulum test was applied to chronic paraplegic patients to investigate the effects of FES-cycling of different duration (20-min vs. 40-min) on biomechanical and electromyographic characterization of knee mobility. Seven adults with post-traumatic paraplegia attended two FES-cycling sessions, a 20-min and a 40-min one, in a random order. Knee angular excursion, stiffness and viscosity were measured using the pendulum test before and after each session. Surface electromyographic activity was recorded from the rectus femoris (RF) and biceps femoris (BF) muscles. FES-cycling led to reduced excursion (p < 0.001) and increased stiffness (p = 0.005) of the knee, which was more evident after the 20-min than 40-min session. Noteworthy, biomechanical changes were associated with an increase of muscle activity and changes in latency of muscle activity only for 20-min, with anticipated response times for RF (p < 0.001) and delayed responses for BF (p = 0.033). These results indicate that significant functional changes in knee mobility can be achieved by FES-cycling for 20 min, as evaluated by the pendulum test in patients with chronic paraplegia. The observed muscle behaviour suggests modulatory effects of exercise on spinal network aimed to partially restore automatic neuronal processes