Specific aspects on the adequate selection of electric equipment intended for use in potentially explosive atmospheres

When using electrical equipment in installations operating in hazardous areas endangered by potentially explosive atmospheres, care shall be taken also to the selection of such equipment. The correct selection of equipment for use in potentially explosive atmospheres must be considered in the design phase of an installation and verified in the mounting phase. Equipment intended for use in potentially explosive atmospheres must be designed, manufactured and placed on the EU market considering the provisions of the ATEX Directive 2014/34/EU. In order to adequately select the electrical equipment, the provisions of the regulations in force must be considered together with the provisions of applicable standards.

An Electric Equipment Analysis Method based on Knowledge Reasoning

Facing the increasingly complex power grid architecture and high equipment failure risk, a comprehensive equipment condition analysis method based on knowledge reasoning is proposed in this paper, mainly using the large amount of characteristic information to realize the condition evaluation, fault detection and early warning. Firstly, it statistically analyzes the historical data, extracts the characteristic information of equipment health status and builds a knowledge mapping library of key factors for equipment-centered status analysis; secondly, it establishes an intelligent early warning library of equipment index data, and gets the probability of equipment defects and fault risks through induction-based knowledge reasoning method; finally, it gets the equipment status rating through logic and rule-based knowledge reasoning method and the closed-loop system of equipment status evaluation is established. The reasonableness of the evaluation method is verified by the examples, which realizes supervision equipment operation status mining early warning, sensing equipment operation status in advance and reducing potential operation risk of power grid.

Implementation of the control algorithm of the traction electric equipment

Currently, in various fields of science and technology, there is a fairly large number of methods for predicting reliability indicators, which differ in the set of tasks to be solved and the features of the mathematical apparatus used. When developing a methodology for calculating the reliability of an electric machine, one of the main stages is the development of a mathematical model, in which it is possible to take into account the factors, the impact of which directly affects the technical condition and the level of operational safety. Taking into account the disturbing influences during the implementation of the technological process is possible provided that the means of automation are used. The performed analysis of statistical data on traction motor failures made it possible to substantiate the advisability of warming up, since a significant number of electrical part failures occur in the autumn - winter - spring periods of time. This feature of the distribution of failures is to a certain extent due to the direct wetting of the insulation and a decrease in its dielectric strength. This process is associated with a sharp drop in external and internal temperatures when placing the locomotive at the depot, which contributes to the appearance of condensate on the insulation, its further destruction. The measure proposed in the article for warming up traction electrical equipment is aimed at an integer reduction in the failures of the electrical part of the electric motors under consideration.

Knowledge Modeling in Troubleshooting

Introduction. The article describes the approach to solving the problem of complex technical system troubleshooting based on expert knowledge modeling. Intelligent information systems are widely used to solve the problems of diagnostics of multilevel systems including combine harvesters. The formal description of the subject domain knowledge is the framework for building the knowledge base of these systems. The sequence of creating an expert system knowledge base in accordance with production rules is considered. Materials and Methods. The approach is founded on the fault function table. As the object of diagnostics, one of the subsystems of the combine harvester electric equipment “opening the hopper roof flaps” is considered. The basis for constructing a sequence of elementary checks is a system of logical equations describing both the serviceable and possible faulty states of the subsystem. Results. A structural logic model is developed. As a result of analyzing the fault function table, the sets of elementary checks are determined. Four criteria have been used to analyze the weight of these checks. The authors have determined optimal sequence of checks and have developed a decision tree, which allows finding the cause of the malfunction and is the basis for creating the knowledge base of an intelligent information system. A fragment of the knowledge base is given. Discussion and Conclusion. The proposed approach of expert knowledge modelling increases the efficiency of the unit for troubleshooting of the intelligent decision support system. It makes possible to structure the base of expertise and establishing the optimal sequence of elementary checks. This allows determining the optimal sequence of application of the knowledge base production rule that makes it possible to reduce the time of restoring the serviceability of combines.

Wired and Cable Networks of Fire-hazardous Zones

The urgency of the issue to be considered is conditioned by the high fire hazard of electric cables of the voltage up to 1000 V. As a result of fires associated with this type of products at the industrial and storage units in 2020 in Russia, the volume of direct loss exceeded 68 % comparing to the fires caused by all other types of cable lines occurred at enterprises. The high fire hazard of industrial facilities is mainly associated with numerous fire-hazardous premises containing combustible media capable of inflammation under the impact of ignition sources. In order to reduce their fire hazard, two independent and mutually reinforcing systems of fire-hazardous premise classification applied at designing such units are developed: Identification of fire-hazardous zone’s class to ensure correct selection of electric equipment and level of its fire-protection. Categorization of buildings and premises as fire-hazardous to ensure fire protection for both for buildings and attending employees. The main regulatory document where the requirements to fire-hazardous zone classification and selection of relevant fire-protected equipment were initially stipulated is the Rules for electric installations (1980). In 2008, the updated version of the Rules was incorporated into the text of the Federal Law of June 22, 2008 № 123-FZ «The technical regulation on fire safety requirements». The studies conducted by the experts of The Federal State Budgetary Establishment «All-Russian Research Institute for Fire Protection of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters» of Russia serves as a basis to develop normative documents regulating categorization of fire-hazardous premises and classification of fire-hazardous zones as well as the recommendations on the scope of application of some electric equipment in fire-hazardous zones. The adopted classification of fire-hazardous zones is unique and has no analogues in global practices.

Electric Equipment Inspection on High Voltage Transmission Line Via Mobile Net-SSD

Daily check and inspection of electrical utilities on the transmission line to find out faults or malfunction data and analyze, it’s to ensure normal state of electrical equipment really difficult in any situation. Machine-controlled inspections by like robots or drones for power transmission infrastructures is an indispensable way to assure the safety of power transmission. Targeted object detection and classification of the power transmission infrastructure is the prerequisite for automatic inspection. In our experiment we have create the dedicated datasets of the electric equipment on power transmission line for multi-object detection, including our data collection, prepossessing and annotation. This work has been done multiple experiments to solve our functional problem and compare novel state of art deep learning methods such as Faster R-CNN, Mask R-CNN, YOLO, and SSD with MobileNet is a base feature extractor, to realize the electric equipment on power transmission line detection. For Condition monitoringand diagnosis identification of the importance of electric equipment on the electric transfer line, in the proposed deep detection approach, the Single-Shot Multi-box Detector (SSD) is a powerful deepmeta-architecture. The results show that our method can automatically detect electric equipment on high voltage transfer defects more accurately and rapidly than lightweight network methods and traditional deep learning methods. Results shed new light on defect detection in actual in progressive scenarios. In our research the main goal to show the implementation of the object detection on electric equipment's inspections on high voltage electric transfer lines on drone video using MobileNet-SSD object detection and recognition.