**4. Research problem description**

Identifying the site or location of damage in the OCP from the indicated types of damage is a difficult task to put into practice (**Figures 2** and **3**).

Selective relays from the main phase to ground faults are separately available in large numbers. Relays are widespread but are characterised by instability. By device stability, we mean the quality of recognition of *SN* semantic situations in transient signals in the OCP, which is necessary for the correct operation of devices in the ASNOM system. To ensure the stability of the relay protection and automation devices, it turns out that in some cases the traditional choice of the settings for the operation of these devices is not enough. The same type of device with the same algorithm is installed in different networks. In each network, high-voltage equipment has its own specifics and operating history, exhaustion factors, etc. Therefore, it is possible to have complex interfering extraneous signals at the installation sites of devices that are present for long periods of time and for which the use of devices is not designed.

The flow of information develops sequentially in the object and can be interrupted and resumed for various reasons. This property of the object during operation can lead the ASNOM stabilisation system to instability.

Studies show that recognising RPA algorithms is often built on the analysis of only one or several *SNs* [16–18]. In the remaining *SN*, the algorithms are blocked during the development of damage to the OCP and do not recognise the change of *SN* and *SN* scenarios. In this case, the available information is not used. However, the change of *SN* and *SN* scenarios allows one to highlight this additional information and use the additional rules of the *DOP* in recognition algorithms (**Figure 4**).

The reasons for the instability of the work are insufficient information to make a decision on the management of OCP, the similarity in structure and content of the main situations of *SN* with interference and unrecognisable *SNs*. It was revealed that volume restrictions may be a consequence of: (1) minimisation and shortcomings of synthesised recognition algorithms; (2) the influence of various conditions of specific sections of the network when introducing devices with the same methods; and (3) the lack of redundancy in the reception and processing of signals. It is known that redundancy of information allows you to work with distortions, extraneous signals, interference and more. This requirement may not be implemented, despite the fact that the amount of information should correspond to the complexity of the task. We will consider ways to address these causes of violations of the recognition of dynamically changing information.

If, when modelling in CAD on a joint model of information sensors TS and an object, it turns out that the signal *S(t)* does not track the gradation of changes in the transient in the OCP and changes roughly, without distinguishing close *SN* situations, this is a sign of insufficient information for recognition. For the success of solving the recognition problem, it is necessary to provide a sufficient amount of information. If it turns out that it is necessary to use the internal coordinate of the object, then it is necessary to ensure the formation of such information. To do this, an additional TS sensor is needed for direct or indirect information acquisition.

Studies show that a residual amount of information is present in the situation recognition tree [16–18]. Information is captured by the FIX unit and evaluated by other algorithms. In the case of continued development of the processes in the OCP, algorithms for additional recognition of information in the DOP block are involved (**Figure 4**). Additional algorithms are also used to clarify the recognition and

#### *Automation and Control*

completion of the amount of information as well as for self-monitoring and partial diagnostics of the object, evaluating its performance, equipment and algorithms that make up the ExS expert system.

The purpose of further study is to obtain an additional amount of information from the OCP for its use in the GESRPA recognition tree to increase the stability of RPA algorithms. To do this, the passage of the elementary component of interest is tracked along the template structures, which are the GESOCP, GESSN, GESASNOM schemes, a separate *SN* situation, a scenario of *SN* changing in time and a tree of possible *SN* developments. It can also be considered from the place of origin to the exit to the OCP through the coordinates of the OCP to the input of the recognition system and then according to the patterns of the GES schemes to the control system output of interest (**Figure 4**).
