Improved ANN Based Tap-Changer Controller Using Modified Cascade-Correlation Algorithm

Artificial neural network (ANN) can play a vital role in its application to on-load tap changer of distribution transformers since the input variation in practical field is enormous due to continuous changes in the power system conditions and neural network is capable of handling this variation due to its robust generalization capability. In this paper we use the two algorithms, namely, scaled conjugate gradient (SCG) and Bayesian regularization (BR) for training an ANN to control the automatic on load tap changer of two parallel transformers connected across the power network. Primarily five data sets are constructed considering voltage phase angle differences between the sources feeding the primary windings while the secondaries are connected in paralleling mode. For each data set, accuracy in ANN based tap changer decision is investigated and BR algorithm yields better results than SCG algorithm. In the second stage, another combined data set is formed by analyzing the trend of false responses in primary results. Results show significant improvement in performance when combined data set is used for training. With this data set, difference in performance between BR and SCG algorithms reduces, however BR still performs better with significantly high accuracy in tap changer operation. The results demonstrate the potential applicability of ANN in tap changer operation of parallel transformers.

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

Artificial neural networks (ANN) are receiving a lot of attention because of their nonlinear mapping ability in the field of short term load forecast (STLF). ANN based STLF model commonly use back propagation algorithm, that may not converge properly, that affects the forecast accuracy. A hybrid approach, based on artificial neural network (ANN) and genetic algorithm (GA) that combines the advantages of each technique is proposed in this research. Genetic algorithm is implemented for the optimization of the architecture of feedforward neural network and selection of its initial weight values. Error back propagation algorithm for the training of the optimized neural network will be implemented. The second stage of this research is related with the complete training of the neural network based on genetic algorithm, using genetic manipulation of chromosomes. The results show that this approach produced better output in terms of enhanced forecast accuracy.

Artificial neural networks (ANNs) were developed very quickly and applied very widely in recent years due to their strong ability to solve the nonlinear problems. The artificial neural network-based method was also widely applied to the geotechnical engineering. The complexity of the geotechnical engineering problems because of the strong nonlinear relationship between knows and unknowns of the problems can be mapped very well by artificial neural networks. Researches on the application of artificial neural network in geotechnical engineering are reviewed and appraised in this paper. All the networks mentioned are trained with the back-propagation algorithm which is widely used by a great number of researchers. Research reveals that the method is feasible and it will be interested for more geotechnical engineers.

Neural Network is a kind of widely used seismic wave travel time auto-picking method. Most commercial software such as Promax often uses Back Propagation (BP) neural network. Here we introduce a cascade-correlation algorithm for constructing neural network. The algorithm’s convergence is faster than BP algorithm and can determine its own network architecture according to training samples, in addition, it can be able to expand network topology to learn new samples. The cascaded-correlation algorithm is improved. Different from the standard cascade-correlation algorithm, improved algorithm starts at an appropriate BP network architecture (exits hidden units), but the standard one’s initial network only includes input layer and output layer. In addition, in order to prevent weight-illgrowth, adding regularization term to the objective function when training candidate hidden units can decay weights. The simulation experiment demonstrates that improved cascade-correlation algorithm is faster convergence speed and stronger generalization ability. Analytically study five attributes, including instantaneous intensity ratio, amplitude, frequency, curve length ratio, adjacent seismic channel correlation. Intersection figure shows that these five attributes have distinctiveness of first break and stability. The neural network first break picking method of this paper has achieved good effect in testing actual seismic data. Key words : Neural network; Cascade-correlation algorithm; Picking seismic first break

It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS) during GPS outages, a novel prediction algorithm of dead reckon (DR) position error is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. DR, GPS position data are first de-noised and compared at different stationary wavelet transformation (SWT) decomposition level, and DR position error data are acquired after the SWT coefficients differences are reconstructed. A neural network to mimic position error property is trained with back-propagation algorithm, and the algorithm is improved for improving its generalization by Bayesian regularization theory. During GPS outages, the established prediction algorithm predictes DR position errors, and provides precise position for VNS through DR position error data updating DR position data. The simulation results show the positioning precision of the BRBP algorithm is best among the presented prediction algorithms such as simple DR and adaptive linear network, and a precise mathematical model of navigation sensors isn’t established. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.285 2 Full Text: PDF

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

Under the strategic framework of global carbon emission reduction, it is urgent to reduce the energy consumption of centrifugal pumps. Monitoring pump’s operational states can effectively help people to understand system’s behavior, and achieve the optimal energy management on pump equipment. In view of installation space, cost, reliability and other factors, it is necessary to find an alternative to the traditional sensor monitoring scheme. Therefore, it is significant to develop a prediction method for the operational state of centrifugal pump using sensorless technology. In this paper, a neural network estimation model based on Bayesian regularization back propagation (BRBP) algorithm is proposed. Compared with the traditional QP estimation model, the new model has better nonlinearity and higher precision. Using power and rotational frequency as input neurons, BRBP model realizes the output of flow rate and head through neuronal calculations. By comparing the predicted flow rate and head values of a multistage centrifugal pump with the measured values at different rotational frequencies, it is found that the mean relative error of flow rate of BRBP model is in the range of 1% to 4%, and the mean relative error of head is in the range of 1% to 2%. The prediction accuracy of BRBP neural network model is obviously higher than that of QP model, which meets the requirements of industrial application.

A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave

This paper proposes a surrogate approach which utilises an supervised neural network to significantly shorten the time required for thermal qualification of electrical machines’ insulation. The proposed approach is based on a feedforward neural network trained with Bayesian Regularization Back-Propagation (BRP) algorithm. The network predicts the winding’s insulation resistance trend with respect to its thermal aging time. The predicted insulation resistance is evaluated against experimental measurements and an excellent match is found. Its trend is used for estimating the sample’s time to failure under thermal stress at various temperatures. The temperature index of the insulating material, predicted by the neural network, matches with an error of just 0.4% margin against the experimental findings.

The cultivation of oil palm has become a major contributor to vegetable oil production, playing a crucial role in meeting the global demand for food in the agricultural industry. The efficiency of palm oil extraction heavily relies on accurate fruit ripeness classification, which is traditionally performed manually. Accurate assessments of oil palm fruit maturity levels before processing are crucial for Malaysian producers and exporters. This report outlines a machine-learning approach for sorting palm fruit by utilizing an artificial neural network (ANN) and optimizing its performance via a genetic algorithm (GA). The research specifies input features such as colour, size, texture and output classes that include the phases of unripe, ripe and overripe palm fruits. The Palm Fruit Database was built by acquiring images of palm fruits through the on-site collection and an e-database called ROBOFLOW and then defining their maturity characteristics through image processing techniques. Using MATLAB software, this study applied three different ANN training methods: the Levenberg-Marquardt backpropagation algorithm, the Bayesian regularization backpropagation algorithm and the scaled conjugate gradient backpropagation algorithm, resulting in accuracy scores of 74.96, 86.19 and 74.97% respectively. The Bayesian regulation has the best performance. The initial accuracy of the ANN test was 77%, but after applying GA optimization, the accuracy improved to 87%, a 10% increase. The proposed approach offers a practical solution for improving the efficiency and accuracy of palm fruit classification in the palm oil industry, ultimately leading to better quality control and increased productivity.

This research presents a comprehensive approach for bearing fault diagnosis using artificial intelligence (AI), particularly through the application of artificial neural networks (ANNs). By integrating these networks into vibration analysis, the approach aims to meet the critical need for prompt fault detection. The methodology comprises three key steps: vibration signal acquisition, feature extraction, and fault classification. Experiments were conducted to acquire vibration signals for the test bearings on a machinery fault simulator. Six time-domain features were extracted using MATLAB, creating a comprehensive dataset for training the ANN models with three algorithms: Levenberg-Marquardt backpropagation (LMBP), scaled conjugate gradient backpropagation (SCGBP), and Bayesian regularization backpropagation (BRBP). The BRBP algorithm achieved the highest correct classification rate (97.2%), followed by LMBP (90%) and SCGBP (83.6%). To evaluate their efficacy in bearing fault classification, these three networks were simulated, revealing that BRBP could predict all four classes of bearings with zero errors.

A novel method for determining the stress-strain constitutive relationship of high-grade pipeline weld zone material

Drying paddy with low-pressure superheated steam (LPSS) can effectively increase the γ-aminobutyric acid content in paddy. This study aimed to investigate the characteristics and mathematical models (MMs) of thin-layer drying of paddy with LPSS. The experimentally obtained data were fitted by nonlinear regression with 5 MMs commonly used for thin-layer drying to calculate the goodness of fit of the MMs. Then, the thin-layer drying of paddy with LPSS was modeled with two machine learning methods as a Bayesian regularization back propagation (BRBP) neural network and a support vector machine (SVM). The results showed that paddy drying with LPSS is a reduced-rate drying process. The drying temperature and operating pressure have a significant impact on the drying process. Under the same pressure, increasing the drying temperature can accelerate the drying rate. Under the same temperature, increasing the operating pressure can accelerate the drying rate. The comparison of the model evaluation indexes showed that 5 common empirical MMs (Hederson and Pabis, Page, Midilli, Logarithmic, and Lewis) for thin-layer drying can achieve excellent fitting effects for a single experimental condition. However, the regression fitting of the indexes by calculating the coefficient(s) of each model showed that the empirical MMs produce poor fitting effects. The BRBP neural network-based model was slightly better than the SVM-based model, and both were significantly better than the empirical MM (the Henderson and Pabis model), as evidenced by a comparison of the training root mean square error (RMSE), testing RMSE, training mean absolute error (MAE), testing MAE, training R2, and testing R2 of the Henderson and Pabis model, the BRBP neural network model, and the SVM-based model. This result indicates that the MMs established by the two machine learning methods can better predict the moisture content changes in the paddy samples dried by LPSS. Keywords: paddy, low-pressure superheated steam, drying, mathematical model, characteristic DOI: 10.25165/j.ijabe.20231601.7810 Citation: Li Y, Che G, Wan L, Zhang Q L, Qu T Q, Zhao F Z. Characteristics and mathematical models of the thin-layer drying of paddy rice with low-pressure superheated steam. Int J Agric & Biol Eng, 2023; 16(1): 273–282.

Accelerating neural network training using weight extrapolations