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feed-forward artificial neural network-Discrete Wavelet Transform Approach to Classify Power System Transients

ELECTRIC POWER COMPONENTS AND SYSTEMS 2013年第6期41卷 586-604页

作者： Beg, M. A. Khedkar, M. K. Paraskar, S. R. Dhole, G. M. SSGM Coll Engn Shegaon MS Dept Elect Engn Shegaon 444203 Maharashtra India St Gadge Baba Amravati Univ Amravati India

Switching transients in power systems are always a concern in studies of equipment insulation coordination. However, with widespread use of sensitive non-linear electronic devices, these transients are capable of degrading the quality of power. Utilities often switch the shunt capacitor banks to cope with sagging voltage levels, thereby generating transients that travel into the network of end users. Capacitor switching can cause over-voltage, resonance, and inadvertent tripping of adjustable speed drives and many other sensitive electronic devices. This article presents a method to distinguish between transients arising out of isolated capacitor switching, back-to-back capacitor switching, load switching, various line faults, and line switching. Discrete wavelet transform of the modal voltage signal is used to extract distinguishing features from the voltage waveform of these events. The detailed coefficients for d1 and d5 level only, obtained from discrete wavelet transform, are processed, mapped, and given to the feed-forward artificial neural network, which classifies the event accordingly. A real power system has been simulated in PSCAD/EMTDC with lines modeled using a frequency-dependent phase model, and its results are then fed to MATLAB (The MathWorks, Natick, Massachusetts, USA) for implementation of the scheme using the feed-forward artificial neural network.

关键词： transients discrete wavelet transform multi-resolution analysis power quality feed-forward artificial neural network

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Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第3期22卷 708-718页

作者： Erkaymaz, Okan Ozer, Mahmut Yumusak, Nejat Karabuk Univ Tech Educ Fac Dept Elect & Comp Sci Karabuk Turkey Zonguldak Karaelmas Univ Dept Elect & Elect Engn Zonguldak Turkey Sakarya Univ Dept Comp Engn Sakarya Turkey

Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.

关键词： Small-world network feed-forward artificial neural network rewiring network topology

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Resilient back-propagation approach in small-world feed-forward neural network topology based on Newman-Watts algorithm

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neural COMPUTING & APPLICATIONS 2020年第20期32卷 16279-16289页

作者： Erkaymaz, Okan Bulent Ecevit Univ Dept Comp Engn Zonguldak Turkey

The scientific researches are focused on network topologies and training algorithms fields because they reduce overfitting problem in artificial neural networks. In this context, we showed in our previous work that Newman-Watts small-world feed-forward artificial neural networks present better classification and prediction performance than conventional feed-forward artificial neural networks. In this study, we investigate the effects of the Resilient back-propagation algorithm on SW network topology and propose a Resilient Newman-Watts small-world feed-forward artificial neural network model by assuming fixed initial topological conditions. We find that Resilient small-world network further reduces overfitting and further increases the network performance when compared to the conventional feed-forward artificial neural networks. Furthermore, it is shown that the proposed network model does not increase the algorithmic complexity as per other models. The obtained results imply that the proposed model can contribute to the solving of overfitting problem encountered in both deep neural networks and conventional artificial neural networks.

关键词： Resilient back-propagation Small-world network feed-forward artificial neural network Newman-Watts algorithm

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Intermittent series direct current arc fault detection in direct current more-electric engine power systems based on wavelet energy spectra and artificial neural network

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IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2022年第3期12卷 197-208页

作者： Thomas, J. Telford, R. Norman, P. J. Burt, G. M. Univ Strathclyde Elect & Elect Engn Dept 99 George St Glasgow G1 1RD Lanark Scotland

The move towards More-Electric Aircraft (MEA) and Engine (MEE) systems has resulted in system integrators exploring the use of Direct Current (DC) for primary power distribution to both reduce energy conversion stages and enable parallelling of non-synchronised engine off-take generation. A prevalent challenge of utilising DC systems is the safe management of arc faults. Arcing imposes a significant threat to aircraft and can result in critical system damage. Series intermittent arc faults in DC systems are particularly challenging to detect due to the lack of a zero current crossing coupled with the intermittency caused by in-flight vibrations. Additionally, several state-of-the-art arc fault detection methodologies fail to address the handling of aircraft system-specific conditions such as dynamic loading and stability requirements. This paper addresses these unique MEA/MEE issues through the proposal of a new generalised real-time arc fault detection methodology (WaSp) based on time-frequency domain-extracted features applied to a feed-forward artificial neural network (ANN). The paper outlines the analysis of arc fault time-frequency domain features. Simulation-based case studies emulating a range of on-board EPS conditions are presented and show the proposed system has the potential for highly accurate and generalised detection performance with fast detection times.

关键词： direct energy conversion time-frequency domain-feature extraction MEA-MEE issues DC distribution systems safe management system integrators fault diagnosis time-frequency analysis aircraft feature extraction direct current arc fault detection methodologies energy conversion stages more-electric aircraft-engine power systems power engineering computing power distribution feedforward neural nets energy conservation jet engines nonsynchronised engine off-take generation arcs (electric) vibrations feed-forward artificial neural network on-board EPS condition in-flight vibrations

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Enhancing performance of the backpropagation algorithm via sparse response regularization

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NEUROCOMPUTING 2015年 153卷 20-40页

作者： Zhang, Jiangshe Ji, Nannan Liu, Junmin Pan, Jiyuan Meng, Deyu Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China 20th Inst China Elect Technol Grp Corp Xian 710068 Peoples R China

The backpropagation (BP) algorithm is the most commonly utilized training strategy for a feed-forward artificial neural network (FFANN). The BP algorithm, however, always leads to the problems of low convergence rate, high energy and poor generalization capability of FFANN. In this paper, motivated by the sparsity property of human neuron' responses, we introduce a new sparse-response BP (SRBP) to improve the capacity of a FFANN by enforcing sparsity to its hidden units through imposing a supplemental L-1 term on them. The FFANN model learned from our algorithm is closely related to the real human and thus its mechanism fully complies with the human nervous system, i.e., sparse representation and architectural depth. Experiments on several datasets demonstrate that SRBP yields good performances on convergence rate, energy saving and generalization capability. (C) 2014 Elsevier B.V. All rights reserved.

关键词： feed-forward artificial neural network Backpropagation Human nervous system Regularization

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New hybrid between SPEA/R with deep neural network: Application to predicting the multi-objective optimization of the stiffness parameter for powertrain mount systems

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JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL 2020年第4期39卷 850-865页

作者： Dinh-Nam Dao Guo, Li-Xin Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China Le Quy Don Tech Univ Control Technol Coll Hanoi Vietnam

In this study, a new methodology, hybrid Strength Pareto Evolutionary Algorithm Reference Direction (SPEA/R) with Deep neural network (HDNN&SPEA/R), has been developed to achieve cost optimization of stiffness parameter for powertrain mount systems. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration of a rear engine mount, mean square displacement of a rear engine mount, mean square acceleration of a front left engine mount, mean square displacement of a front left engine mount, mean square acceleration of a front right engine mount, and mean square displacement of a front right engine mount. A hybrid HDNN&SPEA/R is proposed with the integration of genetic algorithm, deep neural network, and a Strength Pareto evolutionary algorithm based on reference direction for multi-objective SPEA/R. Several benchmark functions are tested, and results reveal that the HDNN&SPEA/R is more efficient than the typical deep neural network. stiffness parameter for powertrain mount systems optimization with HDNN&SPEA/R is simulated, respectively. It proved the potential of the HDNN&SPEA/R for stiffness parameter for powertrain mount systems optimization problem.

关键词： SPEA R algorithm feed-forward artificial neural network extreme learning machine multi-objective evolutionary algorithms powertrain mount system stiffness mounting system

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Exploring the Potentials of artificial neural network Trained with Differential Evolution for Estimating Global Solar Radiation

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ENERGIES 2020年第10期13卷 2488页

作者： Babatunde, Olubayo M. Munda, Josiah L. Hamam, Yskandar Tshwane Univ Technol French South African Inst Technol FSATI Dept Elect Engn ZA-0183 Pretoria South Africa Ecole Super Ingenieurs Electrotech & Elect 2 Blvd Blaise Pascal F-93160 Paris France

The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and R2 of 0.8254.

关键词： global solar radiation feed-forward artificial neural network differential evolution hydrogen production renewable energy

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Modeling of volume and surface area of apple from their geometric characteristics and artificial neural network

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INTERNATIONAL JOURNAL OF FOOD PROPERTIES 2017年第4期20卷 762-768页

作者： Ziaratban, Armin Azadbakht, Mohsen Ghasemnezhad, Azim Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Mech Engn Gorgan Iran Gorgan Univ Agr Sci & Nat Resources Dept Hort Gorgan Iran

Geometric parameters and physical properties of agricultural products are widely used in designing and manufacturing of harvesting devices. These features are highly useful for drying and sorting processes. This can lead to determine the major and minor diameters in this regard. As such, the current study applied machine vision, and image processing technology to identify the major and minor diameters of the Golden Delicious apple. Through applying apple diameters, the actual surface area and real volume of apples were measured by peeling method and water displacement method, respectively. Finally, mathematical modeling, and feed-forward artificial neural network allowed for estimation of the surface area and volume of Golden Delicious apple. The results revealed that the correlation coefficient (R-2) of the mathematical model, for the volume and surface area were 0.9394 and 0.9291, respectively. In the neural network, R-2-values for the volume and surface area in the most appropriate topology were 0.99991 and 0.99995, respectively. Moreover, study findings indicate that predicting the volume and surface area of fruit can be determined better using artificial neural network than using mathematical model. The proposed artificial neural network procedure applied in this study even minimized the complex calculations for estimating volume and surface area of fruit.

关键词： Apple feed-forward artificial neural network Image processing Machine vision Mathematical modeling

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Biometric authentication system based on texture features of retinal images

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INTERNATIONAL JOURNAL OF BIOMETRICS 2018年第3期10卷 195-213页

作者： Mazumdar, Jarina B. Nirmala, S. R. Gauhati Univ Dept Elect & Commun Engn Gauhati Assam India

In biometric authentication system, distinct set of characteristic features are used to identify an authorised person. Retina is a stable biometric feature because of its location and unique physiological characteristics. In this paper, we propose a texture feature-based retinal authentication system. Texture features are considered as important features for authentication purpose. These texture features of retina are extracted using local configuration pattern (LCP) and Radon transform technique. The LCP computes the local structural information as well as the microscopic information of the image. Using Radon transform on retinal images, Radon features are extracted which contains the texture information of the blood vessels. A feature vector is formed by combining all theses LCP and Radon features and then fed to a feed-forward artificial neural network (FANN) classifier. This stage checks whether the test image belongs to the authorised person or not. Three general retinal databases DRIVE, HRF, Messidor, and images collected from two local eye hospitals are considered to authenticate a person. Two retinal authentication databases RIDB and VARIA are also used for evaluating the performance of the system. The results obtained show that the system is effective and efficient in authenticating the individuals.

关键词： biometric texture feature local configuration pattern radon transform feed-forward artificial neural network FANN authentication vessel pattern classifier

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Determining the best and simple intelligent models for evaluating BOD5 of Ahvaz wastewater treatment plant

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DESALINATION AND WATER TREATMENT 2021年 209卷 242-253页

作者： Rahmati, Mojtaba Ghaed Tishehzan, Parvaneh Moazed, Hadi Shahid Chamran Univ Ahvaz Fac Water & Environm Engn Ahvaz *** Iran

The biochemical oxygen demand (BOD5) could be used as an indication of wastewater treatment quality, but measuring BOD5 is very time-consuming and costly. Ahvaz wastewater treatment plant (A-WWTP) plays a pivotal role in reducing the input load to the Karun River and it is very important to check its efficiency. Thus, the most critical parameters affecting the BOD5 were determined using the linear regression and stepwise method. The capability of the multivariate linear regression model (MLR), feed-forward artificial neural network (FF-ANN), and adaptive neuro-fuzzy inference system (ANFIS) were investigated with different architectures and inputs to predict the effluent BOD5 of A-WWTP (for daily and monthly modes). These architectures had two, three, four, or five inputs. The results of the MLR revealed that the maximum correlation coefficients (R) for training and testing were 0.916 and 0.864 (daily), and 0.809 and 0.793 on a monthly basis, respectively. The maximum R in FF-ANN for training and testing was 0.960 and 0.906 (daily basis), and 0.921 and 0.849 (monthly basis), respectively. Meanwhile, the maximum R in ANFIS for training and testing was 0.980 and 0.933 daily, and 0.968 and 0.927 monthly, respectively. The results indicated that the three models are appropriate, but the ANFIS is a more accurate model. In addition, based on conditions and available wastewater qualitative parameters, all of the architectures can be used to estimate the output BOD5.

关键词： Adaptive neuro-fuzzy inference system Biochemical oxygen demand feed-forward artificial neural network Regression Wastewater

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