How to add Gaussian distributed noise with zero mean and standard deviation of 2% ? How to find out SNRdB by using computed noise signal and actual signal?

With reference to the above question, I computed noise signal and SNR based on the actual and noise contaminated signal. Hence, I request you to is this the correct way to compute noise signal and SNR in dB?.

Actual_signal=Ias; % Instantaneous sinusoidal current signal of size (6001*1)
std=0.02; % standard deviation of 2%
meanValue=0; % mean=0
Noise_signal = signal + std*randn(size(signal)) + meanValue;

Amean = mean(Actual_signal);
Bmean = mean(Noise_signal);
SNR = (Amean / Bmean);
SNRdB=10*log10(SNR)

[dpb]

2% of what?

Actual_signal=Ias;     % Instantaneous sinusoidal current signal of size (6001*1)
std=0.02;              % standard deviation of 2%
meanValue=0;           % mean=0
Noise_signal = signal + std*randn(size(signal)) + meanValue;
...

The above adds a random component whose standard deviation is 0.02; a percentage needs to refer to some value that would be the percentage of. Perhaps you want 2% of some measure of the actual signal? That could be rms(), pk-pk, ..., but need to know what.

If it were rms(), then something like

noise_pct=0.02;
signal_rms=rms(Actual_signal);
Noise_signal = noise_pct*signal_rms*randn(size(Actual_signal)) + meanValue;
noisy_signal=signal+Noise_signal;

but you'll have to decide what the actual reference to compare to is...