Multiple Local Maxima Research Articles

Optimizing Solar PV Array Reconfiguration for Maximum Power Extraction Using Hippopotamus Algorithm under Partial Shading

This research paper presents an innovative approach to maximizing power extraction from solar photovoltaic (PV) arrays under partial shading conditions by employing the Hippopotamus Optimization Algorithm (HOA). Partial shading is a common issue that significantly reduces the efficiency of PV systems by creating multiple local maxima on the P-V curve, thereby challenging conventional Maximum Power Point Tracking (MPPT) methods. To address this, we propose an adaptive reconfiguration strategy for the PV array, optimized using HOA, which successfully moderates the impacts of shading and enhances overall energy yield. The Hippopotamus Optimization Algorithm, inspired by the foraging behavior of hippopotamuses, is utilized for its robust global search capabilities and fast convergence. The algorithm dynamically adjusts the arrangement of the PV module to locate and maintain operation at the global maximum power point. Our methodology involves simulating various shading scenarios and evaluating the performance of HOA-based reconfiguration against traditional MPPT techniques. Simulation results demonstrate a significant improvement in power extraction efficiency, with the HOA-based reconfiguration strategy consistently achieving higher power output compared to conventional methods. Additionally, the proposed system exhibits enhanced adaptability to changing shading patterns, ensuring reliable performance in diverse environmental conditions. The findings highlight the potential of the Hippopotamus Optimization Algorithm as a powerful tool for optimizing PV schemes, particularly in scenarios where shading is inevitable. This study contributes to the advancement of renewable energy technologies by offering a novel solution for improving the efficiency and reliability of solar PV arrays.

A novel hybrid GMPPT method for PV systems to mitigate power oscillations and partial shading detection

The Maximum Power Point Tracking (MPPT) algorithm plays a crucial role in maximizing the power output of a PV system. While uniform shading conditions (USCs) present challenges related to dynamic changes in irradiance, partial shading conditions (PSCs) introduce the complexity of multiple local maxima and rapidly changing shading patterns. Conventional algorithms often struggle to effectively track the global maximum power point (GMPP) during PSCs leading to power losses and oscillations around the MPP. In contrast, advanced algorithms are more complex and require additional computational time causing the algorithm to unnecessarily scan the entire PV curve during USCs, thereby extending the tracking time. This paper proposes a hybrid approach that combines the Modified Perturb and Observe (MP&O) and Modified Coot Optimization Algorithm (MCOA) methods termed as MP&O-MCOA as a robust real time MPPT algorithm. The MP&O method with trapezoidal rule and adaptive step size is employed under USCs, while the MCOA with only one tuning parameter and fewer random numbers is utilized during PSCs to rapidly identify varying conditions. Experimental validation using a boost converter has demonstrated the effectiveness of the proposed method, achieving the fastest average tracking times of 0.24 s under USCs and 0.73 s under PSCs and highest average tracking accuracy above 99 % for all operating conditions tested. The proposed method has proven to outperform other existing MPPT methods under various weather conditions due to its ability to distinguish between USCs and PSCs at exceptional speed and accuracy.

Hardware implementation of Firefly algorithm-based maximum power point tracking on Arduino

In this paper, we present the design and implementation of a Firefly Algorithm-based Maximum Power Point Tracking (MPPT) system on an Arduino platform, aimed at optimizing the performance of photovoltaic systems under variable environmental conditions, particularly partial shading. The Firefly Algorithm was chosen for its ability to efficiently locate the global maximum power point in the presence of multiple local maxima caused by non-uniform irradiance. The experimental setup utilized a Boost converter controlled by the Arduino, dynamically adjusting the duty cycle to maintain the PV system at its optimal operating point. Results demonstrated the system's effectiveness in responding to changes in solar irradiance, ensuring consistent maximum power output. Building on this, our research highlights the use of low-cost hardware like Arduino for implementing advanced MPPT algorithms, bridging the gap between theoretical research and practical applications. The study emphasizes that even with limited resources, significant gains in efficiency can be achieved in renewable energy systems. The findings suggest that using such a platform not only reduces costs but also enhances the accessibility of advanced MPPT technologies in real-world applications, making them a viable option for broader adoption.

Tunable elastic wave transmission and resonance in a periodically aligned tube-block structure

A tube-block structure is proposed to realize tunable elastic wave transmission and resonance, consisting of periodically aligned circular tubes sandwiched and joined by two blocks. Finite element simulations for a unit structure are carried out to reveal the frequency dependence of the transmission behavior for the normal incidence of longitudinal and transverse waves in the tube-block structure. As a result, the transmission ratios are found to take multiple local maxima at different peak frequencies. Eigenfrequency analysis shows that the local resonances of the tube and the block surfaces occur at the peak frequencies in the transmission ratios. The peak frequencies originating from the local resonance of the tube depend on its radius and thickness, while those from the resonance on the block surfaces are in good agreement with the theoretical relation between the interval of the periodically aligned tubes and the wavelength of the Rayleigh wave. Furthermore, when the tube-block structure is subjected to compressive loading, the deformation shifts the peak frequencies of the transmission ratio corresponding to the local resonance of the tube. This result implies that the proposed structure has the potential to serve as a tunable meta-interface between solid blocks.

Parallelization of adaptive Bayesian cubature using multimodal optimization algorithms

PurposeBayesian cubature (BC) has emerged to be one of most competitive approach for estimating the multi-dimensional integral especially when the integrand is expensive to evaluate, and alternative acquisition functions, such as the Posterior Variance Contribution (PVC) function, have been developed for adaptive experiment design of the integration points. However, those sequential design strategies also prevent BC from being implemented in a parallel scheme. Therefore, this paper aims at developing a parallelized adaptive BC method to further improve the computational efficiency.Design/methodology/approachBy theoretically examining the multimodal behavior of the PVC function, it is concluded that the multiple local maxima all have important contribution to the integration accuracy as can be selected as design points, providing a practical way for parallelization of the adaptive BC. Inspired by the above finding, four multimodal optimization algorithms, including one newly developed in this work, are then introduced for finding multiple local maxima of the PVC function in one run, and further for parallel implementation of the adaptive BC.FindingsThe superiority of the parallel schemes and the performance of the four multimodal optimization algorithms are then demonstrated and compared with the k-means clustering method by using two numerical benchmarks and two engineering examples.Originality/valueMultimodal behavior of acquisition function for BC is comprehensively investigated. All the local maxima of the acquisition function contribute to adaptive BC accuracy. Parallelization of adaptive BC is realized with four multimodal optimization methods.

Falcon optimiser for optimal design of Even-Odd column for shaded PV array reconfiguration

ABSTRACT The operation of Photo-Voltaic (PV) arrays is often negatively impacted by the presence of shadows, which significantly reduce the generated power. Shadows create multiple local Maximum Power Points (MPP) and a unique global MPP within the shaded array. Therefore, it is crucial to distribute shadows evenly across the PV array surface to optimise power extraction by reconfiguring the shaded modules. This paper presents a novel approach for reconfiguring Solar Photovoltaic (PV) systems using the Falcon optimiser, specifically focusing on Even-Odd column reconfiguration. The objective is to optimise the performance of the PV system by strategically rearranging the columns of PV modules. The proposed technique aims to minimise shading effects and maximise power generation efficiency by identifying the optimal placement of modules. The Falcon optimiser, known for its efficient and effective optimisation capabilities, is utilised to determine the optimal configuration. Extensive simulations and comparisons with other reconfiguration methods are conducted to evaluate the performance of the proposed approach. The results demonstrate that the Even-Odd column reconfiguration based on the Falcon optimiser significantly improves the overall power output and efficiency of the solar PV system compared to alternative methods.

Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms

We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The proposal is based on modified versions of the expectation–maximization (EM) and variational EM (VEM) algorithms, which are based on the genetic approach and allow us to accurately explore the parameter space, reducing the chance to be trapped into one of the multiple local maxima of the log-likelihood function. Their performance is examined through an extensive Monte Carlo simulation study where they are employed to estimate latent class, hidden Markov, and stochastic block models and compared with the standard EM and VEM algorithms. We observe a significant increase in the chance to reach global maximum of the target function and a high accuracy of the estimated parameters for each model. Applications focused on the analysis of cross-sectional, longitudinal, and network data are proposed to illustrate and compare the algorithms.

Interaction of end-gas autoignition and cold wall in closed chamber

The presence of cold walls in spark-ignition (SI) engines could induce the interaction with end-gas autoignition. This interaction is numerically studied in the current work considering detailed chemistry and transport, with emphasis on near-wall flame structures and dynamics, as well as autoignition-affected wall heat flux. Two alternative fuels, hydrogen, and dimethyl ether (DME), are considered. In hydrogen/air mixtures, autoignition first develops in the end-gas and leaves an unburnt near-wall region. The combustion mode in this region then sequentially behaves as spontaneous ignition, laminar flame, and low-to-intermediate temperature reaction. Correspondingly, the contribution of diffusion and convection in the energy budget respectively increases and decreases as the reaction front moves towards the wall. End-gas autoignition also introduces strong pressure waves propagating back-and-forth, which subsequently lead to multiple local maxima on the temporal evolution of wall heat flux. Consequently, the maximum wall heat flux no longer changes monotonically with both the wall temperature and the quenching distance. Furthermore, the role of low-temperature chemistry (LTC) is studied using DME/air mixtures, which exhibits typical multi-stage heat release and Negative Temperature Coefficient (NTC) behaviors. It is also noted that, the influence of pressure wave is minimal when the unburnt mixtures have relatively low energy density, such as at off-stoichiometric conditions and/or lower initial pressures. Specifically: consistent to distinct three-stage homogeneous ignition at certain fuel-lean conditions (ϕ = 0.25) and intermediate initial temperature (typically around T0 = 600–750 K), in the corresponding 1D case three-stage end-gas autoignition respectively evolves into the near-wall cool, warm and hot flame; in the case at relatively low initial pressures, only the first-stage autoignition occurs in the end-gas, and as such LTC slightly increases the wall heat flux and advances flame quenching. As the reactivity of unburnt mixtures becomes higher, LTC and HTC respectively introduce strong and weak knocking combustion. Multiple weak and strong local maxima are observed in the temporal evolution of wall heat flux, both of which changes non-monotonically with the wall temperature.

Nonmonotonous Translocation Time of Polymers across Pores.

Polymers confined in corrugated channels, i.e., channels of varying amplitude, display multiple local maxima and minima of the diffusion coefficient upon increasing their degree of polymerization N. We propose a theoretical effective free energy for linear polymers based on a Fick-Jacobs approach. We validate the predictions against numerical data, obtaining quantitative agreement for the effective free energy, the diffusion coefficient, and the mean first passage time. Finally, we employ the effective free energy to compute the polymer lengths N_{min} at which the diffusion coefficient presents a minimum: we find a scaling expression that we rationalize with a blob model. Our results could be useful to design porous adsorbers, that separate polymers of different sizes without the action of an external flow.

Towards understanding the role of viscoelasticity in microstructural buckling in soft particulate composites

This work investigates the interplay between viscoelasticity and instabilities in soft particulate composites undergoing finite deformation. The composite is subjected to in-plane deformation at various constant strain rates, and experiences microstructural buckling upon exceeding the critical strain level. We characterize the dependence of the critical strain and wavelength on the applied strain rate through our numerical analysis.In the simulations, we employ the single and multiple-branch visco-hyperelastic models. We find that the critical strain and wavelength – characterized by the single-branch model – show a non-monotonic dependence on the strain rate, reaching a maximum at a specific strain rate. Remarkably, different buckling patterns (with different critical wavelengths) can be activated by changing strain rates. The space of admissible buckling modes widens in composites with higher instantaneous shear modulus. In the composites characterized by the multiple-branch model, the critical strain function exhibit multiple local maxima following a superposition of the single-branch responses. Typically, the branch with a larger relaxation time has a more significant effect on the critical strain. Moreover, the local maximum (of the critical strain function) is amplified by increasing the strain–energy factor of the corresponding branch term.Finally, we perform the experiments on the 3D-printed particulate soft composite characterized by a broad spectrum of relaxation times. The comparison of the experimental and simulation results demonstrates the ability of the numerical model to predict the critical buckling characteristics.

A matter of size and shape: Microclimatic changes induced by experimental gap openings in a sessile oak–hornbeam forest

Forest management integrating nature conservation aspects into timber production focuses increasingly on small-scale interventions. However, the ecological consequences of gap cuttings remain ambiguous in oak-dominated forests. In the Pilis Gap Experiment, we analyze how combinations of different gap shapes (circular and elongated), and gap sizes (150 m2 and 300 m2) affect the microclimate and biota of a mature sessile oak-hornbeam forest in Hungary. We first report the changes in direct and diffuse light, soil moisture, daily air and soil temperatures, and relative air humidity in the experimental cuttings in the vegetation season directly following their implementation. Diffuse light had a central maximum and a concentric pattern. Direct light was distributed along a north-south gradient, with maxima in northern gap parts. Soil moisture was determined by gap shape: it increased significantly in the center of circular gaps, with multiple local maxima in the southern-central parts of large circular gaps. Its pattern was negatively related to direct light, and larger spatial variability was present in circular than in elongated gaps. The daily mean air temperatures at 1.3 m increased in all, especially in large gaps. Soil and ground-level temperatures remained largely unchanged, reflecting on light and soil moisture conditions affecting evaporative cooling. Relative humidity remained unaltered. Even though the opening of experimental gaps changed microclimatic conditions immediately, effect sizes remained moderate. Gap size and gap shape were both important determinants of microclimate responses: gap size markedly affected irradiation increase, gap shape determined soil moisture surplus, while soil and air temperatures, and air humidity depended on both components of the gap design. We conclude that 150–300 m2 sized management-created gaps can essentially maintain forest microclimate while theoretically providing enough light for oak regeneration; and that the manipulation of gap shape and gap size within this range are effective tools of adaptive management.

The area-reconstruction h-dome technique and its efficient Python implementation for improved particle size image analysis.

The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and over segmentation would happen if these local maxima were used as seeds for watershed segmentation. In this work, several methods based on morphological reconstruction, including h-dome transform, h-maxima, and area-reconstruction h-dome transform, are introduced to merge, or erase redundant local maxima, and the performance of these methods in avoiding over segmentation is compared. The results show that the area-reconstruction h-dome transform is the most effective method in controlling over segmentation among the evaluated methods. However, the area-reconstruction h-dome transform is achieved by superposition of binary reconstructions at each grayscale level, which is extremely time-consuming and impractical for batch processing. A hybrid pixel-queue algorithm is applied to accelerate the area-reconstruction h-dome transform, and the algorithm is implemented in Cython to further improve the computational efficiency. For a 2592 × 1944 pixel image, on a PC with an Intel Core i5 2.4GHz processor and 8GB RAM, the processing time of the area-reconstruction h-dome transform after acceleration is about 549 ms, which is 249 times faster than the unaccelerated algorithm and 4 times faster than the reconstruction function in the Scikit-image library (an open-source image processing library for the Python programming language) which performs reconstruction by dilation. The accelerated area-reconstruction h-dome transform algorithm was successfully applied to the segmentation of rubber particles in a thermoplastic polyolefin (TPO) compound. RESEARCH HIGHLIGHTS: Techniques for segmenting particles with irregular shapes based on morphological reconstruction are reviewed. A fast algorithm for area-reconstruction h-dome transform is introduced based on Vincent's first approach combined with the pixel queue algorithm and Cython acceleration. The accelerated reconstruction algorithm is 249 times faster than the unaccelerated algorithm. The fast area-reconstruction h-dome transform algorithm is successfully applied to rubber particle segmentation of a thermal plastic polyolefin.

Algorithm to extract model parameters of partially shaded photovoltaic modules

Uneven irradiation, due to partial shading, can produce hot spots in photovoltaic modules. A classical solution to avoid hot spot consists in using bypass diodes in antiparallel to series-connected cell groups. This solution brings a new problem: the presence of multiple local maximum power points. We present a simple algorithm for fast extraction of the model parameters of partially shaded photovoltaic panels with bypass diodes. An example of the application of the proposed algorithm is illustrated using the data from a real monocrystalline silicon technology photovoltaic module measured under uniform illumination and partial shading conditions. The possibility of using the algorithm as a practical approximate solution is also discussed. The simulations, using only four parameters, represent reasonably well the measured data.

Design and Implementation of a New Fast and Efficient MPPT Controller under Different Solar Irradiance Conditions

The power-voltage (P-V) characteristic curve of solar photovoltaic (PV) systems operating in partial shading conditions (PSC) is nonlinear and has multiple local maximum peak power (LMPP) points, rendering many of the maximum power point tracking (MPPT) algorithms ineffective at locating global maximum peak power (GMPP) points. This work proposes a novel slime mould algorithm- (SMA-) based MPPT controller to utilise maximum peak power (MPP) from solar PV systems during uniform irradiance conditions (UC) and nonuniform irradiance conditions (NUC). On the basis of the MPP they tracked, tracking time, and power efficiency, MPPT controller performance is assessed through MATLAB simulations and implemented experimentally with dSPACE MicroLabBox under various irradiance conditions. The effective performance of the proposed controller is validated and demonstrated in comparison to existing popular MPPT controllers.

Wormhole inducing inflation with Einstein–Gauss–Bonnet dilaton interaction

A few Euclidean wormhole configurations are presented using both the analytic and numerical solutions of the field equations in four-dimensional Robertson–Walker Euclidean background with Einstein–Gauss–Bonnet dilaton interaction. In one analytic solution we present transition from a wormhole to an exponential expansion with Lorentzian time [Formula: see text] using [Formula: see text] after passing through an era of oscillating Euclidean wormhole. The numerical solutions of the scale factor [Formula: see text] show multiple local maxima and minima about a global minimum for inverse power law potentials, while for exponential potential the wormholes have a single minimum. An inflationary cosmic scenario away from the throat of the wormhole can be obtained from the Hubble parameter and deceleration parameter obtained by curve fit of [Formula: see text] of the numerical solution invoking analytic continuation by [Formula: see text]. The potential is also observed to decay sharply.

Realistic C-arm to pCT registration for vertebral localization in spine surgery

Spine surgeries are vulnerable to wrong-level surgeries and postoperative complications because of their complex structure. Unavailability of the 3D intraoperative imaging device, low-contrast intraoperative X-ray images, variable clinical and patient conditions, manual analyses, lack of skilled technicians, and human errors increase the chances of wrong-site or wrong-level surgeries. State of the art work refers 3D-2D image registration systems and other medical image processing techniques to address the complications associated with spine surgeries. Intensity-based 3D-2D image registration systems had been widely practiced across various clinical applications. However, these frameworks are limited to specific clinical conditions such as anatomy, dimension of image correspondence, and imaging modalities. Moreover, there are certain prerequisites for these frameworks to function in clinical application, such as dataset requirement, speed of computation, requirement of high-end system configuration, limited capture range, and multiple local maxima. A simple and effective registration framework was designed with a study objective of vertebral level identification and its pose estimation from intraoperative fluoroscopic images by combining intensity-based and iterative control point (ICP)–based 3D-2D registration. A hierarchical multi-stage registration framework was designed that comprises coarse and finer registration. The coarse registration was performed in two stages, i.e., intensity similarity-based spatial localization and source-to-detector localization based on the intervertebral distance correspondence between vertebral centroids in projected and intraoperative X-ray images. Finally, to speed up target localization in the intraoperative application, based on 3D-2D vertebral centroid correspondence, a rigid ICP-based finer registration was performed. The mean projection distance error (mPDE) measurement and visual similarity between projection image at finer registration point and intraoperative X-ray image and surgeons’ feedback were held accountable for the quality assurance of the designed registration framework. The average mPDE after peak signal to noise ratio (PSNR)–based coarse registration was 20.41mm. After the coarse registration in spatial region and source to detector direction, the average mPDE reduced to 12.18mm. On finer ICP-based registration, the mean mPDE was finally reduced to 0.36 mm. The approximate mean time required for the coarse registration, finer registration, and DRR image generation at the final registration point were 10 s, 15 s, and 1.5 min, respectively. The designed registration framework can act as a supporting tool for vertebral level localization and its pose estimation in an intraoperative environment. The framework was designed with the future perspective of intraoperative target localization and its pose estimation irrespective of the target anatomy.Graphical abstract

Towards green energy for sustainable development: Machine learning based MPPT approach for thermoelectric generator

The rapidly increasing demand for electrical energy and the depletion of fossil fuels ignited research in renewable energy generation and management. Thermoelectric generation (TEG) systems as a green energy resource are designed to recover waste heat recovery. Concentrated solar tackles the disadvantages of low power generation efficiency. This paper investigates the feasibility of using machine learning (ML) based MPPT technique, to harvest maximum power of a centralized TEG system under various operating conditions. The environment-dependent issues such as the non-constant distribution of heat, loss to the heat transfer coating between sinks and sources, and mechanical faults create non-uniform current generation and impedance mismatch causing power loss. In this article, we have proposed a feed-forward neural network (FNN) trained by a novel flow direction algorithm (FDA) with a tuned PID controller to harvest the energy under non-uniform temperature gradient conditions. The non-uniformity of physical conditions generates multiple local maxima's (LMs) in electrical characteristics of TEG systems. Common gradient-based MPPT techniques are unlikely to track true GMPP. As a solution to this non-linear problem, novel FNN-FDA is tested under non uniformly distributed temperature (NTUD) over heat source surfaces under varying load and temperature conditions. In this study, certain contributions to the field of TEG systems were made by tackling the issues such as GMPP tracking, low efficiency, and oscillations around GMPP. The results are compared to the particle swarm optimization (PSO), Barnacle mating optimization (BMO), and grey wolf optimization (GWO) algorithm. Five experiments under different weather conditions are performed. Results are validated and proved with experiments and MATLAB/SIMULINK. The experimental results demonstrated that FNN-FDA performs significantly better when compared with other control techniques.

Calibrating the soil organic carbon model Yasso20 with multiple datasets

Abstract. Soil organic carbon (SOC) models are important tools for assessing global SOC distributions and how carbon stocks are affected by climate change. Their performances, however, are affected by data and methods used to calibrate them. Here we study how a new version of the Yasso SOC model, here named Yasso20, performs if calibrated individually or with multiple datasets and how the chosen calibration method affects the parameter estimation. We also compare Yasso20 to the previous version of the Yasso model. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single-dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to multiple local maxima in the likelihood space. The comparison showed that the resulting model performed better with the validation data than the previous version of Yasso.

Exact Time-Dependent Solutions and Information Geometry of a Rocking Ratchet

The noise-induced transport due to spatial symmetry-breaking is a key mechanism for the generation of a uni-directional motion by a Brownian motor. By utilising an asymmetric sawtooth periodic potential and three different types of periodic forcing G(t) (sinusoidal, square and sawtooth waves) with period T and amplitude A, we investigate the performance (energetics, mean current, Stokes efficiency) of a rocking ratchet in light of thermodynamic quantities (entropy production) and the path-dependent information geometric measures. For each G(t), we calculate exact time-dependent probability density functions under different conditions by varying T, A and the strength of the stochastic noise D in an unprecedentedly wide range. Overall similar behaviours are found for different cases of G(t). In particular, in all cases, the current, Stokes efficiency and the information rate normalised by A and D exhibit one or multiple local maxima and minima as A increases. However, the dependence of the current and Stokes efficiency on A can be quite different, while the behaviour of the information rate normalised by A and D tends to resemble that of the Stokes efficiency. In comparison, the irreversibility measured by a normalised entropy production is independent of A. The results indicate the utility of the information geometry as a proxy of a motor efficiency.

Spatiospectral lobe characteristics for F404 engine noise

Spatiospectral lobes are a currently unexplained phenomenon seen in noise radiation from tactical aircraft. These lobes can be seen as either multiple peaks in the noise spectra at a given field location or multiple local maxima in the directivity when observing the radiated noise field at a particular frequency. Using hybrid beamforming of a 120-microphone near-field array, the characteristics of these lobes are studied for a GE F404 engine installed on a T-7A aircraft and at different engine conditions. Both the measured field and field reconstructions show multiple spatiospectral lobes. While the overall directivity of the noise shifts forward with increase in frequency, individual lobes appear, shift aft, and then disappear as frequency is increased. Individual lobes were also raytraced back to the jet centerline to determine an apparent acoustic source location. For frequencies where multiple lobes are present, they generally radiate from an overlapping maximum source region that moves upstream with frequency. This corroborates the characteristics of spatiospectral lobes observed in other aircraft. [Work supported by ONR.]