Investigation of thermoelectric generators connected in different configurations for micro-grid applications

Thankakan, Rakesh; Nadar, Edward Rajan Samuel

doi:10.1002/er.4015

Cited by 18 publications

(4 citation statements)

References 29 publications

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“…Mismatch power losses (%) = P TEM(max)i − P TEM(max) (17) where N indicates the number of modules in an array configuration.…”

Section: Figure 13mentioning

confidence: 99%

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Investigation of novel thermoelectric sensor array configurations operating under non‐uniform temperature distribution conditions for the measurement of maximum output power in an energy harvesting system

Thankakan¹,

Nadar²

2021

IET Science Measure & Tech

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This research work proposes a configuration study of thermoelectric module (TEM) sensor array operating under uniform and non-uniform temperature distribution (NUTD) conditions. The four different (10 × 10) TEM array configurations, which are electrically connected in series-parallel (SP), all-tied (AT), bridge-joint (BJ), and beehive (BH), are considered to evaluate its performance under six cases of NUTD conditions. The electrical characteristics of the various sensor array configurations has been analysed and tested under both temperature distribution conditions using MATLAB-Simulink. A comparison of results of the various configurations is carried out in terms of maximum output power, voltage and current corresponding to maximum power, utilization factor, losses, relative error, standard deviation, and root mean square error. The maximum output power obtained from various cases of NUTD conditions confirm that the performance of bridgejoint configuration is best than all other configurations in the field of thermoelectric energy harvesting systems.

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“…Mismatch power losses (%) = P TEM(max)i − P TEM(max) (17) where N indicates the number of modules in an array configuration.…”

Section: Figure 13mentioning

confidence: 99%

“…In actual applications, the performance of TEG depends on the boundary conditions of thermal transfer and their configuration [17, 18]. By decreasing the thermal resistance between thermoelectric modules (TEMs) helps to enhance the conversion efficiency and the output power [19].…”

Section: Introductionmentioning

confidence: 99%

Investigation of novel thermoelectric sensor array configurations operating under non‐uniform temperature distribution conditions for the measurement of maximum output power in an energy harvesting system

Thankakan¹,

Nadar²

2021

IET Science Measure & Tech

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“…In terms of capacity configuration, MEGs should arrange the optimal capacity scheme of energy production (EP) devices, energy conversion (EC) devices, and energy storage (ES) devices to achieve the optimal energy supply 6 . Thankakan et al 7 discussed five different configurations to determine the optimal series‐parallel configuration. Khan et al 8 evaluated the economic, energy, and environmental cost of a solar integrated microgrid.…”

Section: Introductionmentioning

confidence: 99%

Three‐level energy flexible management strategy for micro energy grids considering multiple uncertainties at different time scales

Huang

Tan

et al. 2020

Int J Energy Res

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This paper considers the co-optimization operation problem of the multiple micro energy grids (MEGs), and proposes a novel concept of the flexible boundary. Then, a three-level co-optimization model of reconfiguration, dispatching, and reserve is designed considering different time scales. Furthermore, an improved ant colony algorithm based on the information entropy and the chaotic search is proposed. The Shenzhen international low-carbon park in China is selected as an example. The results show that: (a) The capacity configuration scheme considering the flexible boundary conditions could decrease the reserve capacity of power, heating, and cooling from the upper energy grid, namely, 75%, 50%, and 75%, and delay 537 × 10 4 ¥ the investment cost of the new capacity by utilizing the complementary characteristics between different MEGs. (b) The proposed three-level co-optimization model could establish the multi-level optimal cooperative operation schemes of multi-MEGs and decrease the reserve power from the upper energy grid and incentive-based demand response. Among them, UPG provides MEG 1, MEG 2, and MEG 3 with cooling load reserve capacity reduction of 53.95, 85.44, and 38.3 kw h. The positive reserve provided by IBDR for MEG 1, MEG 2, and MEG 3 decreased by 30.53, 20.21, and 52 kW h. (c) The proposed improved ant colony algorithm can consider the operational characteristics of each energy entity in different stages of multi-MEGs and obtain the global optimal operation scheme. Compared the traditional ant colony algorithm, the average convergence times decrease by 18% and the optimization degree of the objective value increase by 12.38%. (d) The sensitivity analysis shows the conditional value at risk method can formulate the optimal dispatching schemes of multi-MEGs according to the risk attitudes of decision makers. When confidence degree is between 0.3 and 0.8, the decision-makers have a high-risk sensitivity and are more willing to avoid uncertain risks of wind power plant and

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“…Hence, a single‐stage conversion of heat energy to electrical energy is possible. In the FISO converter, each input has nine TEMs that are connected in (3 × 3) array square series–parallel (SSP) configuration [40] operating under NUTCs.…”

Section: Introductionmentioning

confidence: 99%

Investigation of a novel multi‐input‐single‐output DC–DC converter topology with GWO‐based MPPT controller for energy harvesting using Seebeck generators at different thermal gradients

Thankakan¹,

Nadar²

2020

IET power electron.

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This research work proposes an energy harvesting system using Seebeck generators (SGs) with a novel multi‐input‐single‐output (MISO) DC–DC converter and grey‐wolf optimisation (GWO)‐based maximum power point tracking (MPPT) controller at different thermal gradients. The thermoelectric modules (TEMs) of the SGs convert the thermal energy dissipated by the stator windings of the wind generator (WG) to electrical energy. The proposed MISO power converter topologies have been analysed for two‐inputs, four‐inputs, n‐inputs, and a single‐output for producing higher output power to power a DC micro‐grid. It has the benefit of higher voltage gain and lower voltage stresses on metal–oxide–semiconductor field‐effect transistor switches. The GWO‐based MPPT is employed in this work that overcomes the drawbacks of traditional MPPT methods such as steady‐state oscillation, transients, global convergence capability, and lower MPPT tracking accuracy under non‐uniform temperature conditions. The MPPT controller has effectively tracked a maximum power with a higher tracking accuracy of 98.19%, and the converter has an efficiency of 90.77% corresponding to the wind velocity of 12.9 m/s of the WG. Also, the TEMs are associated in a square series–parallel configuration, and the MISO power converter is analysed with a GWO‐based MPPT controller.

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Investigation of thermoelectric generators connected in different configurations for micro-grid applications

Cited by 18 publications

References 29 publications

Investigation of novel thermoelectric sensor array configurations operating under non‐uniform temperature distribution conditions for the measurement of maximum output power in an energy harvesting system

Investigation of novel thermoelectric sensor array configurations operating under non‐uniform temperature distribution conditions for the measurement of maximum output power in an energy harvesting system

Three‐level energy flexible management strategy for micro energy grids considering multiple uncertainties at different time scales

Investigation of a novel multi‐input‐single‐output DC–DC converter topology with GWO‐based MPPT controller for energy harvesting using Seebeck generators at different thermal gradients

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