Higher validation accuracy, than training accurracy using Tensorflow and Keras

This happens when you use Dropout, since the behaviour when training and testing are different.

When training, a percentage of the features are set to zero (50% in your case since you are using Dropout(0.5)). When testing, all features are used (and are scaled appropriately). So the model at test time is more robust – and can lead to higher testing accuracies.