Improving the Energy-Conversion Efficiency of a PV–TE System With an Intelligent Power-Track Switching Technique and Efficient Thermal-Management Scheme

Shen, Zhi-Hao; Ni, Hao; Ding, Can; Sui, Guorong; Jia, Hongzhi; Gao, Xiumin; Wang, Ning

doi:10.1109/tcpmt.2021.3074529

Cited by 9 publications

(5 citation statements)

References 31 publications

(30 reference statements)

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“…The inductor voltage of the boost converter can be determined [34] using Eq. (10), which is equal to the input voltage as in Eq. (11).…”

Section: Low Step-up Dc-dc Boost Convertermentioning

confidence: 99%

“…Among the numerous types of renewable sources, photovoltaic (PV)-based electricity generation is one of the best and most widely accepted in recent years [7][8][9]. Even in solar PV systems, a large amount of solar radiation energy is wasted in the form of heat energy, which leads to low energy conversion efficiency [10,11]. Recent research studies have reported the various methods of improving the efficiency of the PV system [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

Kanagaraj¹,

Alansi²

2023

Computer Systems Science and Engineering

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In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI λ D µ ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it's one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system's energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach's performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.

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“…The inductor voltage of the boost converter can be determined [34] using Eq. (10), which is equal to the input voltage as in Eq. (11).…”

Section: Low Step-up Dc-dc Boost Convertermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

Kanagaraj¹,

Alansi²

2023

Computer Systems Science and Engineering

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“…Typical battery energy management, when combined with a PV system, maximizes self-consumption but doesn't resolve battery deterioration or grid overload [4]. It is suggested to develop an effective PV-Thermoelectric (TE) hybrid system with intelligent power-track switching and thermal control based on energy conversion [5]. PV's faces instability problems such as instability in time, location, weather conditions and solar radiation, cost of installation of the PV system is also very high [6].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle in Smart Grid Integrated Photovoltaic Environment with Storage System

MANISSEGARANE¹,

RAJAN²

2023

Preprint

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This research introduces a novel hybrid sliding mode controller integrated with the artificial neural network (SMC-ANN) used for purpose of real power exchange using Intelligence Power Management System (IPMS). This paper's purpose is to explore the transient and steady state voltage, transient current, transient power of EV, along with the EV torque and EV speed. In giving a nonlinear signal that simulates the network to "glide" across a cross-section of its normal behavior, sliding mode control is a nonlinear control strategy that affects a dynamic of a nonlinear system. The setup consists of utility grid, integrated Photovoltaic source (PV) energy with Battery Storage System (BSS). A high gain DC/DC boost converter is implemented to interface BSS to the DC-bus. The Power Conditioning System receives the DC output voltage from the PV panel (PCS). These converters' bidirectional nature offers the benefit of power transfer between storage systems, loads, and PV systems. The results obtained using newly developed algorithm is compared with the conventional sliding mode controller (SMC) and it is seen that the a newly developed algorithm produced better outcomes than the conventional one. Simulation is performed using MATLAB software.

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“…In addition to wind, hydro, solar, and hydrogen [7,8], thermoelectric generation (TEG) also possesses remarkable application potential and value [9] because of its prominent advantages of simple structure, sturdiness, being noiseless, long service life, etc. TEG has been applied in some industrial production, such as in automobile engines [10], geothermal energy exploitation [11], natural gas boilers [12], solar thermoelectric cooling systems [13], combined heat and power generation [14], wearable devices [15], energy-autonomous sensors [16], etc. However, the low energy conversion efficiency of TEG systems given unpredictable and ineluctable heterogeneous temperature distribution (HgTD) is the main obstacle that limits its larger scale and wider range of application [17].…”

Section: Introductionmentioning

confidence: 99%

Dynamic reconfiguration for TEG systems under heterogeneous temperature distribution via adaptive coordinated seeker

Chen

Yang

Guo

et al. 2022

Prot Control Mod Power Syst

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A thermoelectric generation (TEG) system has the weakness of relatively low thermoelectric conversion efficiency caused by heterogeneous temperature distribution (HgTD). Dynamic reconfiguration is an effective technique to improve its overall energy efficiency under HgTD. Nevertheless, numerous combinations of electrical switches make dynamic reconfiguration a complex combinatorial optimization problem. This paper aims to design a novel adaptive coordinated seeker (ACS) based on an optimal configuration strategy for large-scale TEG systems with series–parallel connected modules under HgTDs. To properly balance global exploration and local exploitation, ACS is based on ‘divide-and-conquer’ parallel computing, which synthetically coordinates the local searching capability of tabu search (TS) and the global searching capability of a pelican optimization algorithm (POA) during iterations. In addition, an equivalent re-optimization strategy for a reconfiguration solution obtained by meta-heuristic algorithms (MhAs) is proposed to reduce redundant switching actions caused by the randomness of MhAs. Two case studies are carried out to assess the feasibility and superiority of ACS in comparison with the artificial bee colony algorithm, ant colony optimization, genetic algorithm, particle swarm optimization, simulated annealing algorithm, TS, and POA. Simulation results indicate that ACS can realize fast and stable dynamic reconfiguration of a TEG system under HgTDs. In addition, RTLAB platform-based hardware-in-the-loop experiments are carried out to further validate the hardware implementation feasibility.

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Improving the Energy-Conversion Efficiency of a PV–TE System With an Intelligent Power-Track Switching Technique and Efficient Thermal-Management Scheme

Cited by 9 publications

References 31 publications

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

Electric Vehicle in Smart Grid Integrated Photovoltaic Environment with Storage System

Dynamic reconfiguration for TEG systems under heterogeneous temperature distribution via adaptive coordinated seeker

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