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This paper aims to make use of a reinforcement learning method in order to compute an approximation of the optimal control strategy for a set of reactive compensators in power system to maintain acceptable voltage profile and some elements of power system state vector within operating limits under load variations and system contingencies with the minimum usage of reactive power resources. Optimal...
In this paper, a new voltage coordination model based on agent technology is proposed. In this work, communication simulation of peer-to-peer device coordination has been developed based on the Java agent development (JADE) platform which provides a FIPA-compliant agent platform and a package to development of multi-agent systems. In this paper each STATCOM has been modeled as an intelligent agent...
Mid-term load forecasting is taken into account as one of the most important policies in the electricity market and brings about many financial, commercial and, even, political benefits. In this paper, artificial neural networks are represented for mid-term load forecasting of Iran national power system. To do so, the multi layer perceptron (MLP) neural network as well as radial basis function (RBF)...
Voltage stability is one of the major concerns in competitive electricity markets. In this paper, RBF neural network is applied to predict the static voltage stability index and rank the critical line outage contingencies. Three distinct feature extraction algorithms are proposed to speedup the neural network training process via reducing the input training vectors dimensions. Based on the weak buses...
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