Multilayer Feed-forward Artificial Neural Network Research Articles

Damping of subsynchronous oscillations using adaptive controllers tuned by artificial neural networks

Artificial neural networks (ANNs) are utilised to adapt the controller gains of two widely used control schemes i.e. static VAr compensators (SVC) and excitation controllers (EC), for the damping of subsynchronous resonance (SSR) on a power system. To have good damping characteristics of SSR modes over a wide range of operating conditions, the parameters of the adaptive controllers are adapted based on generator loading conditions. Multilayer feedforward artificial neural networks (ANNs) are developed to serve for the purpose of controller parameter adaptation. The inputs to the ANN include the real power output P and reactive power output Q which characterise generator loading conditions. The outputs from the ANN are the desired controller gains. Time domain simulations are also performed on the IEEE first benchmark model to demonstrate the effectiveness of the proposed adaptive control schemes.

An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities

This paper describes a modular artificial neural network (ANN) based hourly load forecaster which has already been implemented at 20 electric utilities across the US and is being used on-line by several of them. The behavior or the load and its correlation with parameters affecting it (e.g. weather variables) are decomposed into three distinct trends of weekly, daily, and hourly. Each trend is modeled by a separate module containing several multi-layer feed-forward ANNs trained by the back-propagation learning rule. The forecasts produced by each module are then combined by adaptive filters to arrive at the final forecast. During the forecasting phase, the parameters of the ANNs within each module are adaptively changed in response to the system's latest forecast accuracy. The performance of the forecaster has been tested on data from these 20 utilities with excellent results. The on-line performance of the system has also been quite satisfactory and superior to other forecasting packages used by the utilities. Moreover, the forecaster is robust, easy to use, and produces accurate results in the case of rapid weather changes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Accelerating Learning of Neural Networks with Conjugate Gradients for Nuclear Power Plant Applications

The method of conjugate gradients is used to expedite the learning process of feedforward multilayer artificial neural networks and to systematically update both the learning parameter and the momentum parameter at each training cycle. The mechanism for the occurrence of premature saturation of the network nodes observed with the back propagation algorithm is described, suggestions are made to eliminate this undesirable phenomenon, and the reason by which this phenomenon is precluded in the method of conjugate gradients is presented. The proposed method is compared with the standard back propagation algorithm in the training of neural networks to classify transient events in neural power plants simulated by the Midland Nuclear Power Plant Unit 2 simulator. The comparison results indicate that the rate of convergence of the proposed method is much greater than the standard back propagation, that it reduces both the number of training cycles and the CPU time, and that it is less sensitive to the choice of initial weights. The advantages of the method are more noticeable and important for problems where the network architecture consists of a large number of nodes, the training database is large, and a tight convergence criterion is desired.

Automatic ranking of fuzzy numbers with the criterion of a decision-maker learnt by an artificial neural network

In a previous work, we indicated that Artificial Neural Networks (ANN) would be able to learn to compare fuzzy numbers as a real decision maker does. In this paper, we describe in detail the experiment that we have developed to that goal, and in which we have obtained good results. We apply this trained ANN to some decision problems with fuzzy environment, by means of the automatic ranking of the decision problem utilities, performed as trapezoidal fuzzy numbers. So, we use the trained ANN as a personal method to compare fuzzy numbers. We have trained a multilayer feedforward ANN with the criterions (to compare fuzzy numbers) of three people, each with different characteristic, using the backpropagation algorithm and different structures. Then we use this trained ANN to rank a set of fuzzy numbers which can be considered as utilities of decision problems with fuzzy environment, hence enabling us to make the best choice. Several examples are shown also.

Scheduling of hydroelectric generations using artificial neural networks

An approach based on artificial neural networks (ANNs) is proposed for the scheduling of hydroelectric generations. The purpose of hydroelectric generation scheduling is to figure out the optimal amounts of generated powers for the hydro units in the system for the next N (N=24 in the work) hours in the future. Input data include system hourly loads and the natural in flow of each reservoir. In the proposed ANN approach, a clustering ANN is first developed to identify those days with similar hourly load patterns and natural inflows. These days with similar load patterns and inflows are said to be of the same group. A total of four groups are used in the work. Then a multilayer feedforward ANN is developed for each group to reach a preliminary generation schedule for the hydro units. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible suboptimal schedule which satisfies all practical constraints. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of 10 hydro plants. It is concluded that the proposed approach is very effective in reaching proper hydro generation schedules. Moreover, the proposed approach is much faster than conventional dynamic programming approach.

Analysis of electromechanical modes using an artificial neural network

An approach based on artificial neural networks is proposed for the analysis of electromechanical modes in Taiwan power system. To evaluate the dynamic performance of a power system in system operation and planning, the dominant eigenvalues for the worst-damped electromechanical mode must be computed. A multilayer feedforward artificial neural network is developed. It is well known that eigenvalues are complicated functions of many system variables such as bus loads, line flows, generation schedule, bus voltages etc. An important procedure in neural network design is to select those features which most affect system eigenvalues. A clustering artificial neural network is thus designed for feature selection. To demonstrate the effectiveness of the approach, results from eigenvalue analyses of Taiwan power system are reported.

Approximation properties of a multilayered feedforward artificial neural network

We prove that an artificial neural network with multiple hidden layers and akth-order sigmoidal response function can be used to approximate any continuous function on any compact subset of a Euclidean space so as to achieve the Jackson rate of approximation. Moreover, if the function to be approximated has an analytic extension, then a nearly geometric rate of approximation can be achieved. We also discuss the problem of approximation of a compact subset of a Euclidean space with such networks with a classical sigmoidal response function.

Optimal mapping of neural-network learning on message-passing multicomputers

In this paper, we study the optimal mapping of the learning process in multilayer feed-forward artificial neural networks (ANNs) on message-passing multicomputers, with an objective of minimizing the completion time of the given learning algorithm. This optimization problem is NP-hard in general and cannot be solved directly even for a small number of neurons. By observing the dominance of the computation time of a parallel neural-network learning algorithm over its communication time, we present a novel approximation algorithm for mapping large neural networks on multicomputers, given a user-specified error degree that can be tolerated in the final mapping. The target ANNs we study are learned by a static learning rule, such as the back-error-propagation learning algorithm. We study both static and dynamic mapping schemes for systems with static and dynamic background workload. Experimental results for mapping on systems with static background workload, which include a network of Sun workstations and an Intel iPSC/2 Hypercube multicomputer, are found to be very close to those predicted by analysis. Experimental results for mapping on multicomputers with dynamic background workload are obtained by simulations.

Modelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection technique

Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can be trained to forecast flow dynamics in a water distribution network. Such flow dynamics can be compared with water demands in a particular district metered area. The objective of this study was to model flow dynamics in four district metered areas of the City of Harare, Zimbabwe using the ANNs technique in an effort to detect systems leakages. A multi-layer feed-forward back-propagation artificial neural network was used for modelling the flow and simulate water demand using a Matlab Neural Network Toolkit. The difference between actual water consumed (metered consumption) and the simulated water demand for a district metered area represents the water leakage in the water distribution network of the district metered area. It was discovered that an ANN could be trained and be used to forecast flow with up to 99% confidence. Thus, ANNs technique is a flexible and efficient approach to detection of leakages in water distribution networks.Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages