This paper addresses the problem of decision-making support for the modernization of distributed automated control systems (ACS) in power engineering by proposing an integral quality criterion that combines similarity-driven Markov process modeling with geometric programming. The methodology transforms the transition rate matrix of a continuous-time Markov chain (CTMC) into a matrix polynomial, enabling the derivation of normalized similarity indices and the development of a criterion-based model to quantify relative variations in system quality without requiring global optimization. The proposed approach yields a generalized criterion model that facilitates the ranking of modernization alternatives and the evaluation of the sensitivity of optimal decisions to parameter variations. The practical implementation is demonstrated through updated state transition graphs, quality functions, and UML-based architectures of diagnostic-ready evaluation modules. The scientific contribution of this work lies in the integration of similarity-based Markov modeling with the mathematical framework of geometric programming into a unified criterion model for the quantitative assessment of functional readiness under multistate conditions and probabilistic failures. The methodology enables the comparison of modernization scenarios using a unified integral indicator, assessment of sensitivity to structural and parametric changes, and seamless integration of quality evaluation into SCADA/Smart Grid environments as part of real-time diagnostics. The accuracy of the assessment depends on the adequacy of transition rate identification and the validity of the Markovian assumption. Future extensions include the real-time estimation of transition rates from event streams, generalization to semi-Markov processes, and multicriteria optimization considering cost, risk, and readiness.

In the article "Analysis of the Use of Decentralized Electric Grids with Small Modular Reactors," the latest approaches to energy security and the resilience of electrical systems through the integration of small modular reactors (SMRs) into decentralized electric grids are examined. In the context of global challenges such as climate change, rising demand for electricity, and the need to reduce greenhouse gas emissions, decentralized energy supply systems are becoming increasingly relevant. The article begins with an overview of the technological aspects of small modular reactors, including their design and operating principles. The authors emphasize the advantages of SMRs, such as compactness, high safety, the possibility of modular manufacturing, and flexibility in placement. They also discuss the potential of SMRs to ensure energy independence for regions and to mitigate risks associated with centralized electricity generation. Subsequently, the article analyzes the economic and environmental benefits of decentralized electric grids. In particular, it highlights the reduction in electricity transportation costs and the opportunities for integrating renewable energy sources alongside SMRs. The authors explore existing models for integrating SMRs into decentralized systems, analyzing their functioning in various countries and regions. A separate section of the article addresses the challenges that arise from implementing decentralized electric grids with SMRs. These include regulatory barriers, technological risks, and safety concerns. The authors propose possible solutions and strategies for overcoming these obstacles, emphasizing the importance of international cooperation and knowledge sharing. The concluding part of the article contains recommendations for further research in this field, particularly in the direction of developing new technologies and regulatory frameworks that will facilitate the effective integration of small modular reactors into decentralized electric grids. This article will serve as a valuable resource for energy professionals, researchers, and policymakers seeking to implement innovative solutions for a sustainable energy future.

The article "Research on Modern Methods and Tools for Diagnosing High-Voltage Equipment" examines key aspects of diagnosing and monitoring high-voltage systems used in energy and industry. High-voltage equipment, such as transformers, switching devices, and cable lines, is critical for ensuring the reliability of electrical networks. Failures in these systems can lead to significant economic losses, interruptions in electricity supply, and even emergency situations. The article focuses on modern diagnostic methods, including non-invasive technologies such as thermography, acoustic emission, and ultrasound testing. These methods allow for the early detection of defects, reducing the risks of serious issues arising. Special attention is given to technologies for measuring and analyzing electrical parameters, which ensure timely detection of deviations in equipment performance. The authors also analyze innovative monitoring tools, including systems based on the Internet of Things (IoT). These systems can provide round-the-clock monitoring of the condition of high-voltage equipment, allowing for prompt responses to changes in its operation. The prospects of using artificial intelligence and machine learning to improve diagnostic accuracy and predict potential failures are also discussed. The article presents the results of practical studies that confirm the effectiveness of modern diagnostic methods in real operational environments. It highlights the role of diagnostics in maintenance planning and asset management, which helps reduce costs and enhance the reliability of energy systems. Thus, the article emphasizes the importance of implementing modern diagnostic technologies in the field of high-voltage equipment, outlining the main advantages, challenges, and prospects for their application. This makes it a valuable resource for professionals, engineers, and researchers working in this field.