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The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads...
This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal...
In this paper a new neuro-genetic approach for fault section estimation (FSE) in electrical power system is presented. We propose a procedure to obtain objective function (required for fault section estimation) using an artificial neural network (ANN). The genetic algorithm (GA) optimization method is then employed to estimate the fault section making use of the objective function. The Hebb's learning...
The effect of learning rate (LR) on the performance of a newly introduced evolutionary algorithm called population-based incremental learning (PBIL) is investigated in this paper. PBIL is a technique that combines a simple genetic algorithm (GA) with competitive learning (CL). Although CL is often studied in the context of artificial neural networks (ANNs), it plays a vital role in PBIL in that the...
Considering the features of long term load forecasting are complicated, this paper proposes a generic neural network model that is able to adapt to and learn from amount of non-linear or imprecise rules, so it is a model with highly robustness. For avoiding the inflexibility of the generic neural network itself, many experiences and opinions of experts are introduced during the use, so that a comprehensive...
This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy...
Frequency is an important parameter in power system monitoring, control and protection. This paper shows how a combination of neural networks and genetic algorithm can be used to estimate power system frequency. Neural networks on the other hand offer great advantages in learning, adaptation, fault tolerance and parallelism. Genetic algorithm is a parallel global search technique that emulates natural...
A comprehensive evaluation approach of power quality (PQ) based on subordinate degree-BP neural network was proposed in this paper. In case the BP neural network training process is trapped by the local minimum point, genetic algorithm (GA) was introduced to optimize the network's initial weights. A large number of samples based on the random-distribution theory were produced to train the network,...
In the competitive electricity market, building optimal bidding strategies for generation companies (Gencos) could need to evaluate some market parameters such as rivalpsilas strategic bidding behavior, forecasting load and others. These parameters have the characteristic of uncertain variables in randomness and fuzziness. In the past work, due to the limit in mathematical theory, the model developed...
The increasing electrical load penetration on power systems requires the development of researching on short-circuit current level for large scale power grid, and the problem of short circuit currents has become significant for the planning and operation of the power system.This paper analyses short-circuit current situation as well as the development tendency in China in detail, and proposes a new...
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