Assurance of Data Faultiessness in Automated Analysis of the Technical and Economic Indicators for Power Unit Boiler Installations

摘要

Abstract— The performance efficiency of automated intelligent systems (AISs), which provide the managing personnel of electric power systems not only with systematically ordered information about the technical state of equipment and installations but also with recommendations on arranging their operation, maintenance, and repair, depends first of all on the safety and faultlessness of the relevant databases. Continuous monitoring of the indicators characterizing the technical state of equipment and installations involves high costs that are far from always being justified. Therefore, in most frequent cases, these indicators are determined from the results of tests and emergency and scheduled repairs. In fact, this information is discrete in nature and is entered in the database from dedicated logbooks. The urgency of automated settling of matters concerned with arranging maintenance and repair becomes even more important in view of the fact that no less than half of the main equipment and installations operating in electric power systems have worked out their fleet life in many respects. The use of indicators like thermal efficiency margin or permissible number of short circuit fault clearances by a circuit breaker for technical state management purposes leads to a higher risk of making erroneous decisions under these conditions. Therefore, the urgency of the problem of ensuring the safety and faultlessness of AIS databases does not decrease with time but, on the contrary, constantly tends to become more important. As an example of incorrectness of the existing approach to recognition of gross errors, the article considers data on the monthly average values of technical and economic indicators of the boiler installations of gas-and-oil fired 300-MW power units. It is pointed out that the sample of monthly average values of technical and economic indicators is inconsistent with the representative sample from the general totality of data. An interval checking method and a checksum method for recognizing gross errors have been developed and approbated. The first method is based on comparing the realizations of technical and economic indicators with their possible variation interval, and the second method is based on comparing the estimated and real annual average values of the realizations of technical and economic indicators. By using the proposed methods, it is possible to decrease the risks of elaborating erroneous recommendations, making erroneous decisions, and spending excessive costs.