Improving the performance of the MPPT for thermoelectric generator system by using Kalman filter

Yahya, Khalid; Bılgın, Mehmet Zeki; Erfidan, Tank; Çakır, Bekir

doi:10.1109/iceee2.2018.8391315

Cited by 15 publications

(10 citation statements)

References 5 publications

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“…It has also been discovered that it affects the accuracy in TEG output calculations or current gradient. As provided in [8,9,12,13], increased perturbation steps will result in high steady-state oscillation around the MPP. This will reduce TEG power production.…”

Section: Algorithm For Maximum Power Point Tracking (Mppt)mentioning

confidence: 98%

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A Thermoelectric Energy Harvesting System

Yahya¹,

Salem²,

Iqteit³

et al. 2020

Renewable Energy - Resources, Challenges and Applications

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Thermoelectric generators (TEGs) and their applications have gained momentum for their ability to use waste thermal energy. More contemporary technology must offer more exceptional energy-efficient applications at a lower cost. New technology must also have an ability to generate electric power through the conversion of wasted heat. The TEG has demonstrated its efficiency and how it can offer increased potential by adding an MPPT algorithm to increase the power flow while decreasing the cost of operation. The limitations can be offset by the use of lower cost manufacturing materials and automated systems in the TEG units. It is also important to note the cost per watt found in using a thermoelectric generator is estimated to be $1/W for an installed device. To achieve this goal, the optimum operating point should be monitored by DC to DC converters. The DC to DC converters should also be driven through a generated pulse using an MPPT algorithm.

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Section: Algorithm For Maximum Power Point Tracking (Mppt)mentioning

confidence: 98%

“…Because of the linearity characteristic of the TEG devices, most of the researchers are shedding light on the MPPT algorithm. Based on an open-circuit voltage and a short-circuit current [7][8][9], the optimum value of the generated power is half of the short-circuit current (I sc )/open-circuit voltage (V oc ) of TEG.…”

Section: Energy Harvesting Systemmentioning

confidence: 99%

A Thermoelectric Energy Harvesting System

Yahya¹,

Salem²,

Iqteit³

et al. 2020

Renewable Energy - Resources, Challenges and Applications

Self Cite

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“…A Kalman filter is an estimator which provides an optimal stochastic estimation in a noisy environment 30,31 . The mechanism of Kalman filter relies on two core stages; time updating and measurement updating.…”

Section: Kalman Filtermentioning

confidence: 99%

A new maximum power point tracking algorithm based on power differentials method for thermoelectric generators

Yahya

Alomari

2020

Int J Energy Res

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Summary This study uses a new maximum power point tracking (MPPT) algorithm for Thermoelectric Generator (TEG) devices. The MPPT algorithm appears as an essential solution due to the nature and the variation characteristics of the TEG devices under certain conditions. In this paper, the power differentials‐maximum power point tracking (PD‐MPPT) algorithm is proposed to control the boost converter by measuring the output power of TEG devices at both the start and finishing points of the power curve along with making a comparison of these two measured power points. The priority is given to the highest power point until the maximum power point is achieved, and Kalman Filter has been applied to eliminate the oscillation generated from the TEG system. This algorithm does not require any extra circuit to measure the short‐circuit current or the open‐circuit voltage because there is no disconnection between the TEG and the load. The hardware implementation of the power differentials algorithm is demonstrated under steady‐state conditions. Moreover, the PD‐MPPT is an effective and applicable algorithm applied to grab the maximum power point from the Photovoltaics PVs and TEGs systems. The practical experiment is conducted using the “STM32f429” microcontroller to implement the algorithm. During the experiments, the change in the duty cycles is observed. The experimental results show that the PD‐MPPT algorithm performs better under a steady‐state and has the ability to track the maximum power point accurately.

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“…In this sense, the Kalman-MPPT design with a parallel FPGA implementation represents a good fit to estimate non-measurable signals with a fast convergence and a well-balance in area-time performance. In comparison with traditional implementations, such as the perturb & observe (P&O) that is widely used in industry, Kalman filter has a faster tracking response [48], [49]. The filtering capabilities of the Kalman filter allow using more inexpensive sensors for the implementation of DC microgrids [47].…”

Section: Introductionmentioning

confidence: 99%

An FPGA Kalman-MPPT Implementation Adapted in SST-Based Dual Active Bridge Converters for DC Microgrids Systems

et al. 2020

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The design of digital hardware controllers for the integration of renewable energy sources in DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum power point tracking algorithm is implemented in an FPGA and adapted in a dual active bridge (DAB) converter topology for DC microgrids. This approach uses the Hardware/Software (HW/SW) co-design paradigm in combination with a pipelined piecewise polynomial approximation design of the Kalman-maximum power point tracking (MPPT) algorithm instead of traditional lookup table (LUT)-based methods. Experimental results reveal a good integration of the Kalman-MPPT design with the DAB-based converter, particularly during irradiation and temperature variations due to changes in weather conditions, as well as a goodbalanced hardware design in complexity and area-time performance compared to other state-of-art FPGA designs. INDEX TERMS DC-DC power converters, Power generation, Field programmable gate arrays.

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Improving the performance of the MPPT for thermoelectric generator system by using Kalman filter

Cited by 15 publications

References 5 publications

A Thermoelectric Energy Harvesting System

A Thermoelectric Energy Harvesting System

A new maximum power point tracking algorithm based on power differentials method for thermoelectric generators

An FPGA Kalman-MPPT Implementation Adapted in SST-Based Dual Active Bridge Converters for DC Microgrids Systems

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