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Investigation of temperature gradient between ambient air and soil to power up wireless sensor network device using a thermoelectric generator Khalil Azha Mohd Annuar; Ramizi Mohamed; Yushaizad Yusof
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.22463

This paper proposes a study of an energy harvesting system for powering wireless sensor network (WSN) devices. The thermal energy harvesting system used is based on the thermal energy source between ambient air at the soil surface with five depth levels. Measurement was taken for 46 days in a garden area located in Melaka, Malaysia. A feasibility study of soil temperature measurement to obtain a temperature gradient can be used for harvesting by using thermoelectric generators (TEG) modules. Then, the efficiency of TEG with several different configurations based on temperature gradient data has been tested in the laboratory. The results revealed that the depth of soil 6 cm between sensors 1 and 3 will gave the best representation of level average temperature different around 1 ℃. Based on the temperature gradient data, the combination of three TEG SP1848 in a series connection with DC-DC step-up circuit DC1664 will produce an optimum voltage output of about 3 V. This output voltage is enough to operate low power IoT device derived from thermal energy.

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring Khairuddin Khalid; Azah Mohamed; Ramizi Mohamed; Hussain Shareef
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring Khairuddin Khalid; Azah Mohamed; Ramizi Mohamed; Hussain Shareef
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring Khairuddin Khalid; Azah Mohamed; Ramizi Mohamed; Hussain Shareef
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

Optimal placement of grid-connected photovoltaic generators in a power system for voltage stability enhancement Zetty Adibah Kamaruzzaman; Azah Mohamed; ramizi mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp339-346

The high penetration of photovoltaic (PV) generation can cause many technical issues such as power quality and impact on the power system voltage stability. To improve voltage stability and reducing power loss in a power system with PV generators, appropriate planning of PV generators is considered by optimal placement of PV. Thus, an effective heuristic optimization technique such as the Wind Driven Optimization (WDO) technique is applied for determining optimal location of PV generators in a power system. For determining the optimal location of PV, the objective function considers maximizing the Improved Voltage Stability Index. The proposed method for optimal location of PV generators is implemented on the IEEE 118 and 30 bus transmission systems and the 69-radial distribution system. The optimization results show that integrating PV into the test systems improves voltage stability in the system. Comparing the performance of the WDO with the particle swarm optimization technique, it is shown that the WDO technique gives faster convergence.

Modelling of a Low Frequency Based Rectangular Shape Piezoelectric Cantilever Beam for Energy Harvesting Applications Ramizi Mohamed; Mahidur R. Sarker; Azah Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp290-295

Harvesting few amount of charge from environmental ambient sources namely, wind, thermal, heat, vibration, solar utilizing micro scale energy harvesting devices, offers vast view of powering for numerous portable low power electronic devices. However, power harvesting using piezoelectric crystal from low power ambient source nowdays has increasing popularity with the advantages of low cost, long life time, stability and clean energy. Recent trends have shown that most researchers are interested in designing a low resonance frequency vibration based energy harvesting devices despite of its challenges ahead. In this paper, a low frequency based rectangular shape piezoelectric cantilever beam has been developed for energy harvesting applications. The proposed vibration based low frequency cantilever beam using piezoelectric element has been developed by finite element analysis (FEA) employing COMSOL Multiphysics platform. The main goal of the study is to analyze the outcome of geometric model of a piezoelectric cantilever beam and to calculate the resonance frequency of the structure according to its length. The material of PZT-5H, has been considered to enhance the efficiency of the low frequency based cantilever beam. Finally, the proposed result is compared with other existing works.

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