Reliability analysis of distribution network of mining enterprises electrical power supply based on measure of information uncertainty

Abstract

Abstract.In the reliability analysis of the distribution network, information entropy is considered as the measure of uncertainty. The simple mathematical model that allows determining an entropy of a network structure is offered. The model is based on Shannon's approach to the determination of information. Reliability analysis comes down to the comparison of two components of entropy: the first is the entropy of connection a sourceelectroreceiver in the structure; the second is a boundary entropy as information of observance of the requirement to support the required reliability level of electrical power supply of the electroreceiver. The example and the procedure how to perform the necessary calculations for the analysis are provided. IntroductionThe design and operation of systems of external power supply of mining enterprises are aimed to ensure the no-break operation of electroreceivers. One of the criteria of viability evaluation of the system is its reliability in electricity power supply of customers. The regular monitoring of system operation of electrical power supply, data processing and analysis allow designers and operators to develop effective solutions for compliance with a specified level of reliability.There are many schemes of electrical power supply of mining enterprises. Let's address to the schemes of radial distributive networks, which are used to internal electrical power supply of quarries (mines). Such networks have a simple structure and successfully provide the given level of structural reliability. When analyzing structural reliability of the network, the statistical indices are considered to be the most important: failure flow frequency; the recovery time of damages; the states probabilities of the network elements, etc.When performing reliability analysis of a distribution network of mining enterprises electrical power supply, then promising direction can be considered the estimation of information uncertainty about the state of the structure. The information uncertainty (its measure) is one of the criteria of the reliability evaluation of technical systems.

An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure

“…Traditional reliability research of power communication network is mainly based on graph theory and probability theory. The author [28], [29] regards communication network as a weighted undirected graph. Based on graph theory, the reliability of power communication network topology is studied and optimized.…”

Analytical Notes on Growth of Economic Indicators of the Enterprise

“…In the case of multiplicative growth consideration, entropy H is calculated in nats and, in fact, by the same Shannon' formula (10). This model doesn't exclude from consideration of use of the logarithm, for example, with the base of 2, as confirmed in [16][17][18][19][20]. Let's show the connection between entropies whose values can be obtained for different bases of the logarithm.…”

“…In case of uniform distribution of probabilities n p i 1  of discrete random variables, according to (18), the maximum amount of entropy, that is, the maximum uncertainty of events, is determined.…”

Improving the operation quality of technical systems using information theory models

“…The authors created a mathematical model for determining the information entropy of technical network structures, which allows to improve the operation quality of technical system by evaluating the structural reliability. The model partially implemented in [22][23][24], where the evaluation of structural reliability of a technical system is possible through the process of calculating the information entropy. Analytical calculation of reliability has been used in design and operation due to the strict requirements to provide high reliability of technical systems.…”

“…There is, however, a complete mathematical model to determine the amount of information entropy with its qualitative division for each of the selected states. The model was tested in [22][23][24][25][26] and has the following features: -allows to determine the measure of the entropy with qualitative division into two opposite states of a system: operable and non-operable (for solving problems of system reliability); -takes into account a variety of elements states of the system structure; -allows to monitor the statics of the change of entropy; -don't exclude the possibility of further modernization, the development of new methods and the solving of task of the improving operation quality of technical systems. To implement the model, it is necessary to collect, pre-processing and the subsequent analysis of statistical or diagnostic data.…”