confirmatory factor analysis (CFA) is returning "solution has NOT been found!"

Question

I tried a confirmatory factor analysis with 8 variables and 3 latent variables. it gave the following warning- :

Warning messages:
1: In lav_data_full(data = data, group = group, cluster = cluster,  :
  lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
2: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
  lavaan WARNING:
    The variance-covariance matrix of the estimated parameters (vcov)
    does not appear to be positive definite! The smallest eigenvalue
    (= -2.383743e+03) is smaller than zero. This may be a symptom that
    the model is not identified.
3: In lav_object_post_check(object) :
  lavaan WARNING: some estimated ov variances are negative

In order to reduce scale the variables into relatively similar scale I used kmean clustering and arranged them in ascending order of numeric scale of 1-7. However, cfa is now unable to converge and I ended up with the following error :

Warning message:
In lav_object_summary(object = object, header = header, fit.measures = fit.measures,  :
  lavaan WARNING: fit measures not available if model did not converge

I tried a confirmatory factor analysis and was expecting a good fit to continue towards SEM modelling.