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.

Industrial wireless sensor and actuator networks are increasingly used in closed-loop control systems. In such systems, packet dropout, channel degradation, and communication delay can deteriorate tracking performance. This paper proposes a Radio-Quality-Aware Model Predictive Control (MPC) strategy for an industrial conveyor drive system operating under time-varying wireless communication conditions. Unlike classical MPC, the proposed controller incorporates radio-channel information, including the signal-to-interference-plus-noise ratio (SINR), packet error rate (PER), and network-induced delay, into the predictive control formulation, thereby adapting the control action to degraded communication conditions. In the MATLAB simulations, an industrial load torque profile based on sensor-measured signals was used, and the wireless communication scenario was divided into three regions representing favorable, moderately degraded, and severely degraded conditions. The results demonstrate that the proposed MPC maintains the angular speed closer to its reference value than the classical MPC, particularly under severe degradation when the SINR decreases to 5 dB. Compared with the classical MPC, the proposed Radio-Quality-Aware MPC reduces the integral of absolute error (IAE) by approximately 90.6%, the root mean square error (RMSE) by 91.4%, the maximum absolute tracking error by 92.7%, and the input variation index by 67.2%, while maintaining a comparable level of control energy. Overall, these results demonstrate that the proposed Radio-Quality-Aware Model Predictive Control framework improves tracking accuracy, actuator smoothness, and system robustness in industrial wireless control applications.

This article details a matrix-based mathematical method to calculate power flows and transmission losses in an electric grid specifically attributable to low-carbon and renewable energy sources (LCRES) (wind, solar, nuclear). The goal is to improve the transparency and reliability of Guarantees of Origin (GO) certificates. Current GO schemes rely on contractual accounting and neglect physical power losses, undermining consumers’ confidence that they receive “clean” energy. The method uses steady-state power flow analysis to derive a power-loss distribution coefficient matrix. This matrix accurately allocates grid losses back to the LCRES generating nodes, complying strictly with electrical engineering principles. It accommodates both time-varying renewable output and stable nuclear generation. The results offer highly accurate loss-attribution data, supporting more verifiable GOs, ensuring fair compensation for losses, and enhancing energy balance accuracy in hybrid power systems.