On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper proposes a scheme for on-line assessment of voltage stability of a power system for multiple contingencies using an Extreme Learning Machine (ELM) technique. Extreme learning machines are single-hidden layer feed- forward neural networks, where the training is restricted to the output weights in order to achieve fast learning with good performance. ELMs are competing with Neural Networks as tools for solving pattern recognition and regression problem. A single ELM model is developed for credible contingencies for accurate and fast estimation of the voltage stability level at different loading conditions. Loading margin is taken as the indicator of voltage instability. Precontingency voltage magnitudes and phase angles at the load buses are taken as the input variables. The training data are obtained by running Continuation Power Flow (CPF) routine. The effectiveness of the method has been demonstrated through voltage stability assessment in IEEE 30-bus system. To verify the effectiveness of the proposed ELM method, its performance is compared with the Multi Layer Perceptron Neural Network (MLPNN). Simulation results show that the ELM gives faster and more accurate results for on-line voltage stability assessment compared with the MLPNN.

Continuous-Time Identification of Nonlinear Systems Using Radial Basis Function Network Model and Genetic Algorithm

The combined use of the Radial Basis Function Network (RBFN) model with pretreated seawater by biomineralization (BSw) was investigated as an approach to improve copper tailings flocculation for mining purposes. The RBFN was used to set the optimal ranges of Ca2+ and Mg2+ concentration at different Ph in artificial seawater to optimize the performance of the mine tailings sedimentation process. The RBFN was developed by considering Ca2+ and Mg2+ concentration as well as pH as input variables, and mine tailings settling rate (Sr) and residual water turbidity (T) as output variables. The optimal ranges of Ca2+ and Mg2+ concentration were found, respectively: (i) 169–338 and 0–130 mg·L−1 at pH 9.3; (ii) 0–21 and 400–741 mg·L–1 at pH 10.5; (iii) 377–418 and 703–849 mg·L−1 at pH 11.5. The settling performance predicted by the RBFN was compared with that measured in raw seawater (Sw), chemically pretreated seawater (CHSw), BSw, and tap water (Tw). The results highlighted that the RBFN model is greatly useful to predict the settling performance in CHSw. On the other hand, the highest Sr values (i.e., 5.4, 5.7, and 5.4 m·h–1) were reached independently of pH when BSw was used as a separation medium for the sedimentation process.

Instantaneous learning is a desirable feature in neural networks. This type of learning enables the network to be trained very quickly, typically in just one or two passes of the training set as opposed to hundreds or even thousands of passes for networks trained by an iterative process, such as the error backpropagation (BP) algorithm. This dissertation first reviews several existing types of neural networks with instantaneous learning capability. It then proposes a new network called the Fuzzy Classification (FC) Neural Network that can be trained with two passes of the training samples. The first pass assigns the synaptic weights for the input and output layers. The second pass determines the radius of generalization r for each training sample. The network exhibits fuzziness in two regards: (1) by fuzzification of the location of each training vector in the input space; and (2) by assigning fuzzy memberships of output classes to new input vectors. The operation of the FC network is analyzed from different perspectives, namely, separability of patterns, curve fitting and kernel regression. It is shown that one mode of operation of the FC network can be made to behave like a Radial Basis Function (RBF) network by proper choice of membership function and network parameter. The performance of the FC network as a pattern classifier is also discussed in a statistical framework. The generalization performance of the FC network is compared against that of the CC4 Comer Classification Neural Network, the RBF network, and the Multilayer Perceptron network trained by the BP algorithm. Experiments involving a Henon map time series, a Mackey-Glass time series, and a spiral pattern are used in the comparison. The performance of the FC network is found to be comparable to that of RBF and BP networks, and better than that of the CC4 network. Its performance is also more scalable than those of CC4 and RBF networks. Further, the time taken to design an FC network for any given problem is much shorter compared to the other three networks.

In order to accurately depict the complicated characteristics of nonlinear system by modeling, a Radial Basis Function Network (RBFNN) modeling method based on Independent Component Analysis (ICA) is proposed. First ICA is perform for extracting basic features of the training samples, and then the extracted basic features is used to establish to RBFNN model. The simulation indicates that, the hybrid modeling method proposed is better than that of another 2 methods with simple model structure, and is effective and feasible to establish for the nonlinear modeling system.

The existing management structure of medical supply inventory (MSI) is not sufficiently effective, and it is incompetent to solve the problems of medical supply stock control in public security emergencies. Therefore, deep learning and big data technology are employed in this work to optimize the stock control structure and enhance management efficiency, so that the optimized management structure can play an excellent role in the material supply of emergencies. After browsing copious literature, the economic ordering models with infinite/limited supply rate and without shortage are innovatively constructed to realize efficient management of emergency supplies inventory. Besides, the optimized fixed-point and quantitative ordering method of safety stock is employed to construct the MSI models for scarce emergency supplies and the time-sensitive emergency supplies, respectively. Then, an earthquake-related emergency is taken as a case and data source to evaluate the solution results of the emergency MSI model. Moreover, the stacked auto-encoders (SAE) algorithm is used to build the demand prediction model for MSI. Finally, a simulation experiment compares the SAE-based demand prediction model for MSI with a back propagation neural network (BPNN) model and radial basis function network (RBFN) model to verify the model’s performance. The experimental results demonstrate that after 150 times of training, the error between the predicted value and the actual value of each model is within 30, and the prediction accuracy is significantly improved. After 170 times of network training, the mean absolute error (MAE) values of BPNN model and RBFN model are 31.98 and 73.73, respectively. In contrast, the MAE value of the SAE-based model is 21.32, which is superior to the other two models. Evidently, the management structure of MSI is optimized by dividing the emergency MSI into three MSI models for the critical emergency supplies, scarce emergency supplies, and the time-sensitive emergency supplies. The research outcome can provide essential logistical support for dealing with public security emergencies.

Voltage stability has recently become a challenging problem for many power systems. Voltage instability can be ascribed to the lack of reactive power support needed to maintain the voltage profile at a specified value. It has been responsible for severe system disturbances including major blackouts. A key concept in the restructuring of the electric power industry is the ability to accurately and rapidly quantify the capabilities of transmission systems. Computation of available transmission capability (ATC) must take into account adequate static, dynamic, and voltage stability margins. Online voltage stability assessment tools will be required for the secure operation of interconnected power systems. This paper presents the application of artificial neural networks (ANN) for online voltage stability assessment. A key feature of the proposed method is the use of principal component analysis to project the input patterns in the original pattern space into a new subspace having fewer dimensions than the original pattern space. This enhances the efficiency of ANN training. The proposed neural network is used to determine the voltage stability margin of the IEEE 118 bus power system for different operating conditions. The possibility of including topology information in a single ANN is also investigated.

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination (<TEX>$R^2$</TEX>) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

This article explores the use of single trial EEG signals to predict the voluntary movements of single hand and two hands. During single-hand movements, three kinds of task, grasping, releasing, and holding were considered. The tasks considered during two-hand movements are left and right grasping, left and right releasing, and holding. The subject performs the tasks spontaneously without waiting for and responding to any external cues. In addition, a neural adaptive noise canceller is developed that accomplishes eye blinks suppression. The neural adaptive filter is here implemented by means of a three-layer feed-forward neural network. The feature vectors are formed from the three channels (Fz, C3, and F3). We employ the multilayer perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) network with stochastic gradient learning rule for discriminating different patterns of the EEG signals. In the classical approach to RBF and MLP network implementation, the number of hidden units is predetermined. It, usually, results in too many hidden units. To overcome this drawback, we develop an enhanced resource-allocating network (RAN) for discriminating the EEG patterns. These networks start with no hidden units and grow by allocating new hidden units based on the novelty in the EEG signals, which arrive sequentially. The results of this analysis show that the neural networks would be able to detect the movements of a single hand and two hands with an average classification accuracy of 98.82% and 96.40%, respectively. Moreover, the RAN provides a reduction in the training epochs as compared to the MLP and RBF networks. This work represents a promising approach to control prosthesis device.

This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear system. Number of epochs required and time taken to train the controller are shown in the form of bar plots for four neural networks. Levenberg-Marquardt algorithm is used for training the controller using neural network toolbox in MATLAB. Each neural network controller is run ten times. Their performances are compared for each run in terms of number of epochs required and time taken to train each controller for tracking a reference trajectory.

Based on a equivalent model of power network, voltage stability index is obtained from power transfer limitation in this paper. More, considering the regulation capability of voltage source, an on-line voltage instability assessment scheme is proposed. The index is defined to monitor remaining voltage regulation capability of voltage source point. As some voltage source has no enough regulation capability, the impact of loss this voltage supported point on voltage stability is evaluated. Simulation results based on the typical power systems demonstrate that the proposed method can identify the dangerous condition and capture the critical coming voltage source limitation, which adapts to on-line voltage stability assessment.

Hydraulic calculations of water distribution systems are currently performed using computer programs. In addition to the basic calculation procedure, modules responsible for evaluating the obtained calculation results are introduced more and more often into the programs. This article presents the results of research on artificial neural networks with a radial base function (RBF) and a multilayer perceptron (MLP), aimed at determining whether they can be used to model the relationship between the variables describing the computational section of the water distribution system and the diameter of the water pipe. The classification capabilities of the RBF and MLP networks were analyzed according to the number of neurons in the hidden layer of the network. A comparative analysis of RBF networks with multilayer perceptron (MLP) networks was performed. The results showed that the MLP networks have much better classification properties and are better suited for the task of assessing the selected diameters of the water pipes.

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. In such environment, online voltage stability evaluation plays a significant role in secure operation of power systems. This paper presents a new approach for estimating voltage stability security assessment accounting uncertainties in the line parameters and control variables. Security index has been evaluated using Monte-Carlo simulation with & without consideration of unavailability of transmission lines, which is used to contingency selection and compared with well-established methods. Further, evaluation of probabilistic insecurity index (PISI) using cut-set method for single & double line outages at various loading conditions considering voltage stability limit. Static voltage stability limit for various sampled values of system parameters and control variables have been obtained using continuation power flow methodology. The applicability of PISI has been made on-line using radial basis function (RBF). Since voltage security assessment is possible on-line using RBFNN. Proposed algorithms have been implemented on IEEE 6-bus 7-line test systems.

In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the number and distribution of the centers (which are set to the pixels of the observed image) are fixed. In addition, network output is set to the observed image pixel gray scale value. The RBF plays a role of point spread function. The technique can also be applied to image resolution enhancement by generating an interpolated image from the low resolution version. Experimental results show that the learning algorithm can effectively estimate the model parameters and the established neural network model has a high fidelity in representing an image. It is believed that the proposed neural network model provides a valuable tool for image restoration and resolution enhancement and holds promises to improve the quality and efficiency of image processing.

Fatigue crack growth and deformation in weldments of aluminium alloys Hermann, R. Mater. Sci. Eng. A (15 Oct. 1994) 186 (1–2), 45–53