Developing Photovoltaic Cells Parameter Estimation Algorithm Based on Equilibrium Optimization Technique

Nowadays all the world does its best to develop the power generation systems that depend on nature in order to reduce the dependence on fuel. Photovoltaic (PV) systems are considered one of the most important renewable energy resources. Scientific research has gained a high interest, especially in PV cell modeling and parameter estimation. The estimation of optimum parameters for the PV model has been considered the main target of the paper optimization problem. Equilibrium optimization (EO) algorithm is considered one of optimization algorithms inspired from nature physical phenomena. EO algorithm has been inspired from the nature physical process of controlling mass balance through specific volume until reaching equilibrium state. In this paper, an EO algorithm has been proposed and applied to prepare a mathematical model for photovoltaic solar cell. The challenge in this optimization problem is the non-linearity in PV solar cell characteristic. The EO algorithm has been evaluated through the following items. EO has been applied to estimate the parameters of different PV models such as single, double and triple PV models, which have different complexity. Applying the previous item for real PV application. The obtained results have been compared though different functions such as root mean square value and absolute mean error. In all cases, EO obtained results have been compared with the more recent optimization algorithms such as Particle swarm optimization (PSO), Teaching learn Based Optimization (TLBO) and Harries Hawk optimization (HHO). From the all obtained results, EO algorithm gives more accurate PV models in comparison with other optimization algorithms.

Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz

The paper reports on the radio propagation characteristics of Massive MIMO. The realistic measurements are conducted in typical outdoor LOS and NLOS scenarios with the bandwidth of 100 MHz at the carrier frequency of 1.4725 GHz. In this paper the channel propagation in spectrum and space domains are investigated by employing the high-precision parameter estimation algorithm. Based on big data technology, we propose the multipath clustering algorithm and subinterval programming to bring deeper insight into the cluster evolution over the antenna array axis. The works focus on the correlation, and the result is in accordance with the theory of the cluster’s visible region. Furthermore, a non-WSSUS (non-wide sense stationary uncorrelated scattering) channel analytical model is established. The whole research work aims to contribute the radio channel modeling of the 5G Massive MIMO communication system.

Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis

In this paper, we propose an adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis and prediction through the study and analysis of sports videos. The features with more discriminative power are selected from the set of positive and negative templates using a feature selection mechanism, and a sparse discriminative model is constructed by combining a confidence value metric strategy. The sparse generative model is constructed by combining L1 regularization and subspace representation, which retains sufficient representational power while dealing with outliers. To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. Also, different online updating strategies are adopted for the template set and Principal Component Analysis (PCA) subspace to alleviate the drift problem while ensuring that the algorithm can adapt to the changes of target appearance in the dynamic tracking environment. Through quantitative and qualitative evaluation of the experimental results, it is verified that the algorithm proposed in this paper has stronger robustness compared with other classical algorithms. Our proposed visual object tracking algorithm not only outperforms existing visual object tracking algorithms in terms of accuracy, success rate, accuracy, and robustness but also achieve the performance required for real-time tracking in terms of execution speed on the central processing unit (CPU). This paper provides an in-depth analysis and discussion of the adaptive Gaussian incremental expectation stadium parameter estimation algorithm for sports video analysis. Using a variety of county-level algorithms for analysis and multiple solutions to improve the accuracy of the results, we obtain a more efficient and accurate algorithm.

Channel state information-based multi-dimensional parameter estimation for massive RF data in smart environments

Smart environment sensing and other applications play a more and more important role along with the rapid growth of device-free sensing-based services, and extracting parameters contained in channel state information (CSI) accurately is the basis of these applications. However, antenna arrays in wireless devices are all planar arrays whose antenna spacing does not meet the spatial sampling theorem while the existing parameter estimation methods are almost based on the array satisfying the spatial sampling theorem. In this paper, we propose a parameter estimation algorithm to estimate the signal parameters of angle of arrival (AoA), time of flight (ToF), and Doppler frequency shift (DFS) based on the service antenna array, which does not satisfy the spatial sampling theorem. Firstly, the service antenna array is mapped to a virtual linear array and the array manifold of the virtual linear array is calculated. Secondly, the virtual linear array is applied to estimate the multi-dimensional parameters of the signal. Finally, by calculating the geometric relationship between the service antenna and the virtual linear array, the parameters of the signal incident on the service antenna can be obtained. Therefore, the service antenna can not only use the communication channel for information communication, but also sense the surrounding environment and provide related remote sensing and other wireless sensing application services. Simulation results show that the proposed parameter estimation algorithm can accurately estimate the signal parameters when the array antenna spacing does not meet the spatial sampling theorem. Compared with TWPalo, the proposed algorithm can estimate AoA within 3∘, while the error of ToF and DFS parameter estimation is within 1 ns and 1 m/s.

A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

In this paper, a computationally efficient and high precision parameter estimation algorithm with frequency-time combination is proposed to improve the estimation performance for sinusoidal signal in noise, which takes the advantages of frequency- and time-domain algorithms. The noise influence is suppressed through spectrum analysis to get coarse frequency, and the fine frequency is obtained by denoising filtering and using linear prediction property. Then, estimation values of the amplitude and initial phase are obtained. The numerical results indicate that the proposed algorithm makes up for the shortcomings of frequency- and time-domain algorithms and improves the anti-interference performance and parameter estimation accuracy for sinusoidal signal.