ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries

García, Pedro López; García, Carlos A.; Fernández, Luis Martín Sobrado; Llorens, Francisco; Jurado, Francisco

doi:10.1109/tii.2013.2290069

Cited by 226 publications

(121 citation statements)

References 28 publications

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“…This study is actually to support previous results focusing on Neuro-Fuzzy in renewable energies (Garcia, 2014), health monitoring (Agrawal, 2013), developing model (Bisht, 2011), information security (Altaher, 2012), edge detection technique (Bhardwaj, 2013), temperature and humadity system (Ramesh, 2015). Other advantages of the fuzzy can also be found on automatic RNA virus classification (Dogantekin, 2013), and predicting the volumes of Chaotic Traffic (Yeh, 2012).…”

Section: Anfis Testingsupporting

confidence: 74%

Anfis Based Material Flow Rate Control System for Weigh Feeder Conveyor

Rosyada¹,

Misbah²,

Eliyani³

2016

CIS

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Weight control system on the feeder conveyor determines the factor of the quality of products within an industry. The dynamics of the flow rate of material through the feeder conveyor weigh requires a good level of performance controllers. The base of current controllers such as FLC (Fuzzy Logic Controller) requires a certain amount of knowledge and expertise in its design that will make it difficult to achieve good system performance. These difficulties can be overcome by using systems based on ANFIS (Adaptive Neuro-Fuzzy Inference System). By doing the learning offline, using ANFIS can be obtained by fuzzy inference systems to create a controller FLC. Microcontroller have FLC controller program, its integrated with notebook can monitor and control the notebook weigh feeder conveyor system. Designing a system that has been created will give good results with an average error value of 3.86% at the set-point of 1000 grams / minute, and the average error of 5.03% on set-point 2000 grams / minute in ten times testing.

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Section: Anfis Testingsupporting

confidence: 74%

Anfis Based Material Flow Rate Control System for Weigh Feeder Conveyor

Rosyada¹,

Misbah²,

Eliyani³

2016

CIS

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“…Similarly, in order to test the dynamic performance of the proposed controller, the following comparison indexes were used [49]: Mean Relative Error (MRE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Time Square Error (ITSE):…”

Section: Resultsmentioning

confidence: 99%

Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

Hassan

Kamal

et al. 2017

Energies

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An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

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“…battery with super-capacitors [17] - [18], battery with PV/wind or fuel cells [19] - [21] have pre-dominantly used a single battery bank with a dc-dc boost converter, multiport dc-dc converter [22] or sometimes directly in parallel to the inverter dc-link [23]. The primary focus of these research studies was in developing grid-side energy management strategies.…”

Section: Index Terms-mentioning

confidence: 99%

“…Boost Mode (Mode -1): The first mode (Fig. 2(a)) is applicable only when ∑ , < and also the lower limit of i batt,i is limited by the common dc-link current i dc to satisfy the constraint of a boost converter while the upper limit of i batt,i is restricted by the maximum switch stress (v sw ) of a module and the common dc-link current i dc as shown in (20) and (21). The i dc is decided by the power level of the grid-tie converter.…”

Section: A Range Of Operation/ Operational Envelopementioning

confidence: 99%