Voltage Tracking Control of DC- DC Boost Converter Using Fuzzy Neural Network

Hameed, Waleed I.; Sawadi, Baha A.; Muayed, Ali

doi:10.11591/ijpeds.v9.i4.pp1657-1665

Cited by 14 publications

(10 citation statements)

References 13 publications

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“…This process will be repeated through out the PV is feeding the load or grid. The flow chart shown in Figure 4 indicates the power measured at instant is same as power at the preceding instant, that is maximum power point has been achieved and it bypasses the flow [4]. If the variation of power with respect to variation of voltage is greater than zero, then voltage will be altered accordingly.…”

Section: Perturb and Observe Algorithmmentioning

confidence: 99%

“…There is a considerable reduction in space and cost which ISSN: 2088-8694  made the leading manufacturers of PV systems to offer at competitive prices. However, grid connected PV systems should adhere to grid standards and protection of the system to meet safety of the system and operating personnel [4]. The grid connected transformerless inverter PV system has an effect of stray or parasitic capacitances formed between PV panel's aluminum beading and ground which could cause variation in common mode voltage, in turn results huge leakage currents.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network based DC-DC converter for grid connected transformerless PV system

Gurram¹,

Babu

Srinivas³

2022

IJPEDS

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The transformerless photo voltaic (PV) inverter system connected to grid has created a new trend in the energy market due to its reduced space requirement, low cost and increased efficiency when compared to its counterpart i.e with transformer. Transformerless inverter system suffers from common mode leakage currents due to parasitic capacitances between PV panels and ground. However, different new inverter topologies and state of the art modulation strategies are proposed in the literature to counter it. A dc-dc converter is of more significant to maintain the constant PV output voltage at string level and extract maximum power from PV. This paper presents Artificial neural network (ANN) algorithm-based dc-dc converter to track maximum power from PV module connected to grid without transformer. It also compares the performance of ANN based algorithm with conventional perturb and Observe maximum power point tracking (MPPT) technique. MATLAB/Simulink environment is used to pursue the simulation of ANN based algorithm and analyses its performance for variety of irradiance levels.

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Section: Perturb and Observe Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural network based DC-DC converter for grid connected transformerless PV system

Gurram¹,

Babu

Srinivas³

2022

IJPEDS

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“…DC-DC power converters are employed in a variety of applications, including power supplies for personal computer, office equipment, spacecraft power system, laptop computer, and telecommunications equipment, as well as DC motor drives [1]. The DC-DC converter is also an important application for alternative electrical power conditioning as for example photovoltaic, wind generator and fuel cell system [2]. There are three common type of DC-DC converter which are; Buck, Boost and Buck Boost Converter.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of DC to DC boost converter using single phase matrix converter topology

Kadir¹,

Noor²,

Faranadia³

et al. 2020

IJPEDS

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The main objective of this work is to model and simulate DC to DC Boost Converter using Single Phase Matrix Converter (SPMC) topology using MATLAB/Simulink (MLS). The output voltage is controlled by using Pulse Width Modulation (PWM) technique. Four pairs of Insulated Gate Bipolar Transistor (IGBT) is used as the switching device where for each pair, it is located in parallel and opposite direction. Safe commutation technique is performed in preventing voltage spike at the output. Through the simulation, at switching frequency of 25kHz, the model is able to step up its input voltage about two times larger and all of the results achieved a good agreement with the principle of four quadrant operation. It is also realized that without the implementation of safe commutation technique, spikes were generated and the model is unable to boost its input voltage. All of the selected results from the analysis which includes variation of quadrant, switching frequency, duty cycle and resistive load are presented in this paper.

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“…Fuzzy logic is a widely used wideband for control and protection enhance system behavior [21][22]. Some proposals were focused on hybrid fuzzy logic with other techniques for precision improvement of the system control, and decision making for with the protection system [23][24][25]. Many researches were interested in classifying and identifying the fault by a central computer after the data is collected via wireless sensors for monitoring, analyzing, and controlling [26][27].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of wireless system for vibration fault detection using fuzzy logic

Lilo

Mahammad

2020

IJ-AI

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This paper aims at constructing the wireless system for fault detecting and monitoring by computer depending on the wireless and fuzzy logic technique. Wireless applications are utilized to identify, classify, and monitor faults in the real time to protect machines from damage .Two schemes were tested; first scheme fault collected X-Y-Z-axes mode while the second scheme collected Y-axis mode, which is utilized to protect the induction motor (IM) from vibrations fault. The vibration signals were processed in the central computer to reduce noise by signal processing stage, and then the fault was classified and monitored based on Fuzzy Logic (FL). The wireless vibration sensor was designed depending on the wireless techniques and C++ code. A fault collection, noise reduction, vibration fault classification and monitoring were implemented by MATLAB code. In the second scheme the processed real time was reduced to 60%, which is included collection, filtering, and monitoring fault level. Results showed that the system has the ability to early detect the fault if appears on the machine with time processing of 1.721s. This work will reduce the maintenance cost and provide the ability to utilize the system with harsh industrial applications to diagnose the fault in real time processing.

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Voltage Tracking Control of DC- DC Boost Converter Using Fuzzy Neural Network

Cited by 14 publications

References 13 publications

Artificial neural network based DC-DC converter for grid connected transformerless PV system

Artificial neural network based DC-DC converter for grid connected transformerless PV system

Modeling and simulation of DC to DC boost converter using single phase matrix converter topology

Design and implementation of wireless system for vibration fault detection using fuzzy logic

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