7 - Introduction to Gross Error Detection

Abstract

This chapter introduces the issues involved in gross error detection. The chapter describes the basic statistical tests that can be used to detect gross errors. This component of a gross error detection strategy simply attempts to answer the question of whether gross errors are present in the data or not. If measurements do not contain any random errors, then a violation of any of the model constraints by the measured values can be immediately interpreted as due to the presence of gross errors. This chapter also discusses the underlying assumptions, characteristics and relative advantages and disadvantages of various statistical tests. The chapter also highlights the interaction between gross error detection and data reconciliation. Any gross error strategy needs to detect and identify the location of gross errors. There are two types of errors associated with any statistical test: Type I error (when the test detects a nonexistent error) and Type II error (when the test fails to detect an existent error). Only the measurement test and the GLR test can directly identify the location of gross errors by a simple identification rule. The GLR test is the only test that can identify both measurement biases and leaks by the same type of test. The gross error detection strategy by GLR test involves estimation of magnitudes of gross errors. Maximum power tests can be derived for the measurement test and for the nodal tests. However, the GLR test is more powerful than both of them for the single gross error case.