Storage Aided System Property Enhancing and Hybrid Robust Smoothing for Large-Scale PV Systems

Li, Peng; Dargaville, Roger; Cao, Yuan; Li, Danyong; Xia, Jing

doi:10.1109/tsg.2016.2611595

Cited by 51 publications

(14 citation statements)

References 28 publications

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“…Future development of photovoltaic and, in general, of all clean and renewable technologies, is related to the possibility to use energy when is needed. For this reason, energy storage represents a key component in the development of renewable energies [1][2][3][4]. Energy storage makes it possible to meet users needs during out-of-production periods.…”

Section: Introductionmentioning

confidence: 99%

Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System

et al. 2017

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This paper is proposing and analyzing an electric energy storage system fully integrated with a photovoltaic PV module, composed by a set of lithium-iron-phosphate (LiFePO 4 ) flat batteries, which constitutes a generation-storage PV unit. The batteries were surface-mounted on the back side of the PV module, distant from the PV backsheet, without exceeding the PV frame size. An additional low-emissivity sheet was introduced to shield the batteries from the backsheet thermal irradiance. The challenge addressed in this paper is to evaluate the PV cell temperature increase, due to the reduced thermal exchanges on the back of the module, and to estimate the temperature of the batteries, verifying their thermal constraints. Two one-dimensional (1D) thermal models, numerically implemented by using the thermal library of Simulink-Matlab accounting for all the heat exchanges, are here proposed: one related to the original PV module, the other related to the portion of the area of the PV module in correspondence of the proposed energy-storage system. Convective and radiative coefficients were then calculated in relation to different configurations and ambient conditions. The model validation has been carried out considering the PV module to be at the nominal operating cell temperature (NOCT), and by specific experimental measurements with a thermographic camera. Finally, appropriate models were used to evaluate the increasing cell batteries temperature in different environmental conditions.

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Section: Introductionmentioning

confidence: 99%

Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System

et al. 2017

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“…Nonlinear filtering techniques have attracted much attention in signal processing [26,27] and have wide applications in many areas [28][29][30][31]. The classical Kalman filter (KF) is recognized as the best linear filter for linear systems under Gaussian noises.…”

Section: Introductionmentioning

confidence: 99%

“…Although the RLS method offers a fast convergence rate, there exists several problems such as the increase in the computational burden and the decline in the tracking capability [19,20]. The hierarchical identification principle is applied to decompose a bilinear system into several subsystems Nonlinear filtering techniques have attracted much attention in signal processing [26,27] and have wide applications in many areas [28][29][30][31]. The classical Kalman filter (KF) is recognized as the best linear filter for linear systems under Gaussian noises.…”

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confidence: 99%

State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle

Zhang

Ding

et al. 2018

IET Control Theory & Applications

128

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Abstract:In the revised paper, we have highlighted the changes made in Blue FONT. This paper presents a combined parameter and state estimation algorithm for a bilinear system described by its observer canonical state space model based on the hierarchical identification principle. The Kalman filter is known as the best state filter for linear systems, but not applicable for bilinear systems. Thus, a bilinear state observer (BSO) is designed to give the state estimates using the extremum principle. Then a BSO based recursive least squares (BSO-RLS) algorithm is developed. For comparison with the BSO-RLS algorithm, by dividing the system into three fictitious subsystems on basis of the decomposition-coordination principle, a BSO based hierarchical least squares algorithm is proposed to reduce the computation burden. Moreover, a BSO based forgetting factor recursive least squares algorithm is presented to improve the parameter tracking capability. Finally, a numerical example illustrates the effectiveness of the proposed algorithms. IntroductionParameter estimation is a significant part in system identification, and has been widely used in system analysis [1][2][3], system modeling [4][5][6][7], and system control [8,9]. Since many industrial processes are complex and inherently nonlinear, nonlinear system identification has drawn much attention throughout the world [10][11][12]. The bilinear approach for modeling these complex processes are proven to be more precise than any other traditional linear models [13]. However, the nonlinear term existing in the bilinear model brings some challenges for bilinear system identification. During the past decades, much work has been carried out on parameter estimation for bilinear systems. For example, dos Santos et al. presented a subspace identification method for bilinear systems by treating the bilinear term as a second-order white noise process [14]. Larkowski et al. addressed the identification problem of the diagonal bilinear errors-in-variables system and extended the bias compensated least squares technique to bilinear systems [15]. Li et al. applied the polynomial transformation technique to obtain the equivalent input-output representation of the bilinear system and proposed the iterative algorithm for parameter estimation [16][17][18]. The recursive least squares (RLS) approach is known as the most commonly used estimation method among numerous different parameter estimation techniques. Although the RLS method offers a fast convergence rate, there exists several problems such as the increase in the computational burden and the decline in the tracking capability [19,20]. The hierarchical identification principle is applied to decompose a bilinear system into several subsystems Nonlinear filtering techniques have attracted much attention in signal processing [26,27] and have wide applications in many areas [28][29][30][31]. The classical Kalman filter (KF) is recognized as the best linear filter for linear systems under Gaussian noises. However, it is not suitable f...

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“…Environment problems such air pollution and global warming caused by fossil fuels have drawn the world's attention to exploration and utilization of renewable energy sources, in recent years [1][2][3][4][5]. At present, wind energy is the fastest growing renewable energy source and is most prevalent in coastal regions spanning temperate and boreal climates [6][7][8]. There is great potential for wind power development in China, USA, Denmark, and other countries, due to their high average wind velocities [9].…”

Section: Introductionmentioning

confidence: 99%

A PI‐Type Sliding Mode Controller Design for PMSG‐Based Wind Turbine

Zhou

Zhao

et al. 2019

Complexity

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Because the PMSG (permanent magnet synchronous generator)-based WECS (wind energy conversion system) has some uncertainties, the conventional control strategy with poor robustness is sometimes difficult to meet the performance requirements of control. In order to ensure efficient and stability of the system, this paper proposed a novel PI (proportional-integral)-type SMC (sliding mode control) strategy for PMSG-based WECS uncertainties and presented the detailed analysis and design process. Compared with the conventional control method, the PI-type SMC proposed in this paper not only can make the closed-loop system globally stable, but also has a better robustness and slightly reduced the current ripple and distortion. Finally, the simulation results verify the correctness and effectiveness of this algorithm.

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Storage Aided System Property Enhancing and Hybrid Robust Smoothing for Large-Scale PV Systems

Cited by 51 publications

References 28 publications

Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System

Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System

State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle

A PI‐Type Sliding Mode Controller Design for PMSG‐Based Wind Turbine

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