They convert analog signals to binary numbers, and in many cases convert binary results back to analog signals. Problems arise when analog signals are not sampled fast enough. If the signal changes rapidly and the sample time intervals are not small enough, the sampled version of the signal will misrepresent high frequency components of the original signal as low frequency components. This type of signal corruption is called aliasing . Other problems arise from the binary representation of real-world numbers.
Computers represent real numbers with a finite number of binary place values, or wordlength .
In control systems, aliasing or severe finite wordlength errors may cause instability. In monitoring, alarm, and protection systems, such conditions degrade performance. For protection systems in particular, aliasing and finite wordlength errors may cause the system to violate its setpoint accuracy requirements. However, these error sources can be minimized to a point in which they have an insignificant effect on system performance. The following is a summary of methods and practices to minimize aliasing and finite wordlength effects.