Accuracy difference on normalization in KNN
That's a pretty good question, and is unexpected at first glance because usually a normalization will help a KNN classifier do better.  Generally, good KNN performance usually requires preprocessing of data to make all variables similarly scaled and centered.  Otherwise KNN will be often be inappropriately dominated by scaling factors.
In this case the opposite effect is seen:  KNN gets WORSE with scaling, seemingly.
However, what you may be witnessing could be overfitting.  The KNN may be overfit, which is to say it memorized the data very well, but does not work well at all on new data. The first model might have memorized more data due to some characteristic of that data, but it's not a good thing. You would need to check your prediction accuracy on a different set of data than what was trained on, a so-called validation set or test set.  
Then you will know whether the KNN accuracy is OK or not.  
Look into learning curve analysis in the context of machine learning. Please go learn about bias and variance. It's a deeper subject than can be detailed here. The best, cheapest, and fastest sources of instruction on this topic are videos on the web, by the following instructors:

Andrew Ng, in the online coursera course Machine Learning
Tibshirani and Hastie, in the online stanford course Statistical Learning.
I had trained my model on KNN classification algorithm , and I was getting around 97% accuracy. However,I later noticed that I had missed out to normalise my data and I normalised my data and retrained my model, now I am getting an accuracy of only 87%. What could be the reason? And should I stick to using data that is not normalised or should I switch to normalized version.