Improved Perturb and Observation Method Based on Support Vector Regression

Tan, Bicheng; Xia, Ke; Tang, Dachuan; Sheng, Yin

doi:10.3390/en12061151

Cited by 17 publications

(9 citation statements)

References 23 publications

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“…The SVR can be regarded as a linear algorithm in high-dimensional space [88]. The SVR has been proven to be a popular method for regression modeling [89][90][91][92]. In this study, it was constructed using the following SVR function from the training process:…”

Section: Statistical Models For Estimating the Fsvmentioning

confidence: 99%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R 2 = 0.91, RMSE = 35.13 m 3 ha −1 , n = 321) and in the test phase (i.e., R 2 = 0.58, RMSE = 65.03 m 3 ha −1 , and n = 138). This model was followed by the SVR model (R 2 = 0.54, RMSE = 65.60 m 3 ha −1 , n = 321 in training; R 2 = 0.54, RMSE = 66.00 m 3 ha −1 , n = 138 in testing), which was slightly better than the MLR model (R 2 = 0.38, RMSE = 75.74 m 3 ha −1 , and n = 321 in training; R 2 = 0.49, RMSE = 70.22 m 3 ha −1 , and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 10 8 m 3 ) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 10 8 m 3 ). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.The forest stock volume (FSV, m 3 ha −1 ) is the sum of the stem volumes of all the living trees per unit area, and is one of key forest variables for forest resources management and assessments on local, region and country scales [1]. FSVs have a strong relationship with the aboveground biomass (AGB) and carbon stocks [2]. To understand the spatial distribution of carbon in forests and to derive predictions for monitoring carbon stock trends, the FSV must be quantified [3]. Traditionally, the FSV is estimated by sampling several plots, which involves substantial manpower, materials, and financial resources [4]. With the development of remote sensing technology, particularly the Landsat series since 1972, satellite imagery has played an important role in forest inventory. Various studies have been performed to estimate the forest variables ...

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Section: Statistical Models For Estimating the Fsvmentioning

confidence: 99%

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Yang

et al. 2020

Remote Sensing

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“…For example in [13,14,23,24] the descriptions of perturbation and observation, increment conductance, constant voltage and fractional open-circuit voltage MPPT methods are presented. However, papers [25][26][27][28] propose alternative MPPT techniques by means of artificial computational intelligence methods.…”

Section: Thermoelectric Panel With Evolutionary Mpptmentioning

confidence: 99%

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

Golonek

2022

Applied Sciences

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This work proposes the use of a specialized algorithm based on evolutionary computation to the global MPPT regulation of panel of thermoelectric modules connected serially in numerous string sections. Each section of the thermovoltaic panel is equipped with local DC/DC converter controlled by the proposed algorithm and finally this allows the optimization of the total efficiency of conversion. Evolutionary computations adjust PWM signals of switching waveforms of DC/DC sectional simple boost converters, which have outputs configured in parallel. It gives the chance to obtain the highest level of electric energy harvested, i.e., thanks to boost converting operational points precise adaptation to the system temperature profile as well as electric load level. The simulation results of the proposed evolutionary technique confirmed the high speed of the MPPT process that is much better than for perturbation and observation, as well as incremental conductance methods, and it assures concurrent optimization of numerous PWM signals. Next, the work shows practical optimization results achieved by the proposed algorithm implemented to microcontroller module controlling the DC/DC converter during thermal to electric conversion experiment. A laboratory thermovoltaic panel was constructed from a string of Peltier modules and radiator that assured passive cooling. The measurements obtained once more proved the MPPT evolutionary regulation properness and its adaptation effectiveness for different resistive test loads.

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“…Conventionally, MPPT is embedded in a converter to determine the duty cycle that maximizes the PV power yield [22]. A perturbation and observation method (P&O) is used to find the maximum power point [23] by altering the array terminal voltage and then comparing the PV output power with its previous value. If the power increases while voltage increases, the PV array is operating in the correct direction; otherwise, the operational point should be adjusted to its the opposite direction [24,25].…”

Section: Perturbation and Observation (Pando) Mppt Algorithmmentioning

confidence: 99%

High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms

Wang

Chen

et al. 2019

Energies

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The efficiency of a photovoltaic (PV) system strongly depends on the transformation process from solar energy to electricity, where maximum power point tracking (MPPT) is widely regarded as a promising technology to harvest solar energy in the first step. Furthermore, inverters are an essential part of solar power generation systems. Their performance dictates the power yield, system costs and reliable operation. This paper proposes a novel control technology combining discontinuous pulse width modulation (DPWM) and overmodulation technology to better utilize direct current (DC) electrical power and to reduce the switching losses in the electronic power devices in conversion. In order to optimize the performance of the PV inverter, the overmodulation region is refined from conventional two-level space vector pulse width modulation (SVPWM) control technology. Then, the turn-on and turn-off times of the switching devices in different modulation areas are deduced analytically. A new DPWM algorithm is proposed to achieve the full region control. An experimental platform based on a digital signal processing (DSP) controller is developed for validation purposes, after maximum power is achieved via a DC/DC converter under MPPT operation. Experimental results on a PV system show that the DPWM control algorithm lowers the harmonic distortion of the output voltage and current, as well as the switching losses. Moreover, better utilization of the DC-link voltage also improves the PV inverter performance. The developed algorithm may also be applied to other applications utilizing grid-tie power inverters.

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Improved Perturb and Observation Method Based on Support Vector Regression

Cited by 17 publications

References 23 publications

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models

Evolutionary Maximization of Energy Amount Harvested by Means of Panel of Thermoelectric Modules

High Efficiency Solar Power Generation with Improved Discontinuous Pulse Width Modulation (DPWM) Overmodulation Algorithms

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