Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

Lin, Whei-Min; Hong, Chih-Ming; Chen, Chiung-Hsing

doi:10.1109/tpel.2011.2161775

Cited by 297 publications

(98 citation statements)

References 28 publications

(26 reference statements)

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“…The connection results in that the context units always maintain a copy of the previous values of hidden units. Thus, the network can keep the past state, which is useful for applications such as sequence prediction [44][45][46]. In Figure 6, there are 46 neurons in the hidden layer.…”

Section: Ennmentioning

confidence: 99%

Neural Networks for Radar Waveform Recognition

et al. 2017

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For passive radar detection system, radar waveform recognition is an important research area. In this paper, we explore an automatic radar waveform recognition system to detect, track and locate the low probability of intercept (LPI) radars. The system can classify (but not identify) 12 kinds of signals, including binary phase shift keying (BPSK) (barker codes modulated), linear frequency modulation (LFM), Costas codes, Frank code, P1-P4 codesand T1-T4 codeswith a low signal-to-noise ratio (SNR). It is one of the most extensive classification systems in the open articles. A hybrid classifier is proposed, which includes two relatively independent subsidiary networks, convolutional neural network (CNN) and Elman neural network (ENN). We determine the parameters of the architecture to make networks more effectively. Specifically, we focus on how the networks are designed, what the best set of features for classification is and what the best classified strategy is. Especially, we propose several key features for the classifier based on Choi-Williams time-frequency distribution (CWD). Finally, the recognition system is simulated by experimental data. The experiments show the overall successful recognition ratio of 94.5% at an SNR of −2 dB.

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Section: Ennmentioning

confidence: 99%

Neural Networks for Radar Waveform Recognition

et al. 2017

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“…The SN-RBFN's inputs are: the instantaneous conductance (I/V), the incremental conductance (ΔI/ΔV), and the reference voltage error (ΔVpvref(k)=Vpvref(k)-Vpvref(k-1)). In this paper, a supervised learning rule based gradient descent method [11,23] is adopted for the online update of the SN-RBFN parameters. The objective function used for the weights adaptation is defined as:…”

Section: A Mppt Control Of Pv Generatormentioning

confidence: 99%

“…A novel discrete-time NN controller for the control of DC distribution system is designed in [10]. In [11], a RBFN and an improved ENN are proposed as MPPT controllers for different types of RES. A RBFN with an ENN have been also analyzed in [12] for the wind speed prediction in a wind farm.…”

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confidence: 99%

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Chettibi

Mellit

Sulligoi

et al. 2016

IEEE Trans. Smart Grid

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-In this paper, the behavior of a grid-connected hybrid AC/DC Microgrid has been investigated. Different Renewable Energy Sources -photovoltaics modules and a wind turbine generator -have been considered together with a Solid Oxide Fuel Cell and a Battery Energy Storage System. The main contribute of this work is the design and the validation of an innovative online-trained artificial neural network based control system for a hybrid microgrid. Adaptive Neural Networks are used to track the Maximum Power Point of renewable energy generators and to control the power exchanged between the Front-End Converter and the electrical grid. Moreover, a fuzzy logic based Power Management System is proposed in order to minimize the energy purchased from the electrical grid. The operation of the hybrid microgrid has been tested in the Matlab/Simulink environment under different operating conditions. The obtained results demonstrate the effectiveness, the high robustness and the self-adaptation ability of the proposed control system. OWADAYS, the wide diffusion of distributed RES presents a new scenario for the regulation of distribution networks and the availability of new technologies for storage systems encourages their use in power systems [1]. In general, a hybrid AC/DC MG integrates different Distributed Generators (e.g. solar power sources, wind power generators, cogenerators, etc.), a energy storage system and a number of AC and DC loads. A FEC can interface the MG with the electric grid and can operate either in a grid-connected or islanded mode. The use of a PMS is crucial to optimize the power flow through the different components of the MG and the exchange of energy with the electric grid. Moreover, since the power produced by RESs depends on the climatic conditions, MPPT algorithms are needed in order to harvest the maximum available energy. The intermittent nature of RESs with the time-varying loads demand make the use of advanced control structures fundamental in order to make the operation of the MG reliable, economic, and secure under different operating conditions. The MG must also guarantee a high quality power supply to both local loads and electrical grid. Index TermsMany works have focused on hybrid microgrids and have proposed a number of control schemes for different mode of operations [2]- [8]. A multiagent-based energy management system to optimizes the economic operation of a MG is presented in [2]. A reactive power sharing algorithm in hierarchical droop control is developed in [3]. A novel coordinated voltage control scheme with islanding capability for a MG is proposed in [4]. adapt to uncertainties and they can be used also when the model of the system to be controlled is not available. Recently, the NNs with the learning capability are widely applied for the control of complex power systems. In [9], a back-propagation NN is applied for the real-time estimation of the wind speed. A novel discrete-time NN controller for the control of DC distribution system is designed in [10]. In [11], a...

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“…Also, the hill climbing and incremental conductance method [3] uses the derivatives of power or current based on the fact that the sign of derivative is changed on the either side of peak [4]. The neural network (NN)-based MPPT controller improves the tracking efficiency by utilizing the multilayer control structure [5,6]. The extremum seeking control (ESC) [7,8] is proposed with the nearly model-free and self-optimizing control strategy.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Maximum Power Point Tracking Method Based on Three Points Approximation by Digital Controller for PV System

Kim

Bang

Lee

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper proposes the new method based on the availability of three points measurement and convexity of photovoltaic (PV) curve characteristic at the maximum power point (MPP). In general, the MPP tracking (MPPT) function is the important part of all PV systems due to their powervoltage (P-V) characteristics related with weather conditions. Then, the analog-to-digital converter (ADC) and low pass filter (LPF) are required to measure the voltage and current for MPPT by the digital controller, which is used to implement the PV power conditioning system (PCS). The measurement and quantization error due to rounding or truncation in ADC and the delay of LPF might degrade the reliability of MPPT. To overcome this limitation, the proposed method is proposed while improving the performances in both steady-state and dynamic responses based on the detailed investigation of its properties for availability and convexity. The performances of proposed method are evaluated with the several case studies by the PSCAD/EMTDC ® simulation. Then, the experimental results are given to verify its feasibility in real-time.

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Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

Cited by 297 publications

References 28 publications

Neural Networks for Radar Waveform Recognition

Neural Networks for Radar Waveform Recognition

Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid

Real-Time Maximum Power Point Tracking Method Based on Three Points Approximation by Digital Controller for PV System

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