Random Forest+PCA losing accuracy?

Student of data science here. Still learning so I am sorry if this question is too basic for this forum.
I am running Random Forest on a test dataset, with and without PCA.

We were given a task about PCA and random forest.
I am getting a higher accuracy (97%) without the PCA. With PCA I am getting only 93%, even when using all the variance (13 features, the same as the input).

I was was sure that when using all the features, I should get the same results (since the PCA is not actually doing anything). Are my results ok, ir is there some error in the code / something that I need to fix?

This is my code:

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.datasets import load_wine
from sklearn.preprocessing import StandardScaler

(X, y) = load_wine(return_X_y=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0,stratify=y)

clf = RandomForestClassifier(n_estimators=100)
print("Accuracy (no pca):",accuracy_score(y_test, y_pred_rf))

pca = PCA(13)

scaler = StandardScaler()
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)

X_train_pca = pca.transform(X_train_scaled)
X_test_pca = pca.transform(X_test_scaled)


print("Accuracy (with pca)",accuracy_score(y_test, y_pred_rf_pca))

Here is a great chance to practice your debugging skills.
One way is to keep narrowing down the pipeline, until you find exactly where the results start to become different. Usually, the data you think is the same is not the same somehow maybe because rows/columns have switched order.

How do you confirm this?