Single input cerebellar model articulation controller (CMAC) based maximum power point tracking for photovoltaic system

Wu, Min-Kuang; Widodo, Slamet

doi:10.1109/3ca.2010.5533334

Cited by 3 publications

(11 citation statements)

References 9 publications

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“…Same performance can be achieved using the array power instead of the output power, keeping pilot cell for open-circuit voltage and short-circuit current measurement [36]a s illustrated in Fig. 3 yes yes -- [13] yes yes -- [14] yes yes -- [15] yes yes -- [16] yes yes -- [17] yes yes -- [18] yes yes -- [19]y e s --- [29] yes yes -- [33] yes yes -- [34]y e s --- [35]y e s --- [36] yes yes -- [37] yes yes -- [38] yes yes -- [20] non-electrical inputs…”

Section: Stand-alone Ann-based Pv Mppt Techniquesmentioning

confidence: 99%

“…PV-based street lighting, Fig. 20 Single-input CMAC model-based MPPT technique utilising V PV and I PV as inputs [16] a System block diagram b Controller structure GA off-line trained ANN-based MPPT technique utilising G, T and V PV as inputs [27] c System block diagram d Controller structure rural water pumping and residential roof-topped applications concern mainly for minimising the cost to decrease the owners' payback time. Hence [13,17,19,35,38] are most suitable.…”

Section: Suitable Applicationsmentioning

confidence: 99%

“…ANN‐based PV MPPT techniques commonly utilise electric signals as inputs [11–19, 29, 33–38]. Those techniques can benefit from the available electric signals in the system which are: PV array voltage ( V PV ), current ( I PV ) and power ( P PV ).…”

Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

“…Classical MPPT techniques can be adapted with ANN assistant or integration for enhanced performance [13, 17, 29]. Other artificial intelligence techniques can be utilised with ANN offering superior performance on the expense of complexity [12, 14–16, 18, 19, 22, 26, 27, 30, 31, 33, 39, 40, 44, 45]. The following sections describe the behaviour of all the previously mentioned techniques in details.…”

Section: Ann‐based Pv Mppt Techniques Classification According To Tmentioning

confidence: 99%

“…Artificial neural networks (ANNs) feature several advantages, such as robust operation, fast tracking, non‐linear systems tolerant and off‐line training. Therefore various ANN‐based PV MPPT techniques elaborate recently [11–48].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Elobaid

Abdelsalam

Zakzouk

2015

IET Renewable Power Generation

275

118

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Recent researches oriented to photovoltaic (PV) systems feature booming interest in current decade. For efficiency improvement, maximum power point tracking (MPPT) of PV array output power is mandatory. Although classical MPPT techniques offer simplified structure and implementation, their performance is degraded when compared with artificial intelligence-based techniques especially during partial shading and rapidly changing environmental conditions. Artificial neural network (ANN) algorithms feature several capabilities such as: (i) off-line training, (ii) nonlinear mapping, (iii) high-speed response, (iv) robust operation, (v) less computational effort and (vi) compact solution for multiple-variable problems. Hence, ANN algorithms have been widely applied as PV MPPT techniques. Among various available ANN-based PV MPPT techniques, very limited references gather those techniques as a survey. Neither classification nor comparisons between those competitors exist. Moreover, no detailed analysis of the system performance under those techniques has been previously discussed. This study presents a detailed survey for ANN based PV MPPT techniques. The authors propose new categorisation for ANN PV MPPT techniques based on controller structure and input variables. In addition, a detailed comparison between those techniques from several points of view, such as ANN structure, experimental verification and transient/steady-state performance is presented. Recent references are taken into consideration for update purpose.

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Section: Stand-alone Ann-based Pv Mppt Techniquesmentioning

confidence: 99%

Section: Suitable Applicationsmentioning

confidence: 99%

Section: Ann‐based Pv Mppt Techniques Classification According To Cmentioning

confidence: 99%

Section: Ann‐based Pv Mppt Techniques Classification According To Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Elobaid

Abdelsalam

Zakzouk

2015

IET Renewable Power Generation

275

118

View full text Add to dashboard Cite

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Artificial neural network based maximum power point tracking technique for PV systems

Elobaid

Abdelsalam

Zakzouk

2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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The dependency of photovoltaic (PV) arrays on temperature and irradiance levels shapes their known non linear behavior; hence maximum power point tracking (MPPT) is mandatory. Traditional MPPT techniques, like Perturb and Observe (P&O) and Incremental Conductance (IncCond), offer acceptable performance with a trade-off between accuracy and fast operation. Moreover, moderate operation is remarked at rapidly changing environmental conditions. On the contrary, off-line trained artificial neural network (ANN) is considered as accurate, fast and robust estimation technique. In this paper, a two-stage off-line trained ANN based MPPT technique is proposed where two cascaded ANNs are utilized. The first estimates the temperature and irradiance levels from the array voltage and current signals while the other network determines the optimum peak operating point from the temperature and irradiance, estimated by the first ANN. The proposed technique offers enhanced performance even under rapidly changing environmental conditions, no need for temperature/irradiance measurement, in addition to reduced required training sets because of the presented ANN cascaded structure.

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Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid

Kim

2020

Electronics

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This paper presents an overview of the maximum power point tracking (MPPT) methods for photovoltaic (PV) systems used in the Micro Grids of PV systems. In the PV system, the output varies nonlinearly with temperature and radiation, and the point at which power is maximized appears accordingly. The MPPT of the PV system can improve output by about 25%, and it is very important to operate at this point at all times. Various methods of tracking the MPP of the PV system have been studied and proposed. In this paper, we discuss commonly used methods for the MPPT of PV systems, methods using artificial intelligence control, and mixed methods, and present the characteristics, advantages, and disadvantages of each method.

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Single input cerebellar model articulation controller (CMAC) based maximum power point tracking for photovoltaic system

Cited by 3 publications

References 9 publications

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Artificial neural network‐based photovoltaic maximum power point tracking techniques: a survey

Artificial neural network based maximum power point tracking technique for PV systems

Overview of Maximum Power Point Tracking Methods for PV System in Micro Grid

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