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Asymmetry in the supply voltage in three-phase circuits disrupts the flow of currents. This worsens the efficiency of the distribution system and increases the problems in determining the mathematical model of the energy system. Among many power theories, the most accurate is the Currents’ Physical Components (CPC) power theory, which tries to justify the physical essence of each component. Such knowledge can be used to improve efficiency and reduce transmission losses in the power system. This article discusses the method of mathematical decomposition of current components in the case of a three-wire line connecting an asymmetric power source with linear time-invariant (LTI) loads. Special cases where irregularities appear in the results of calculations according to the CPC theory are discussed. The problem of equivalent conductance in the case of a non-zero value of the constant voltage component is discussed. The method of determining symmetrical components for periodic non-sinusoidal waveforms is also discussed. These considerations are supported by numerical examples.

This article discusses fundamental issues associated with the functional reliability of selected fire alarm systems (FASs) in operation during building fires. FASs operate under diverse external or internal natural environmental conditions, and the operational process of FAS should take into account the impacts of physical phenomena that occur during fires. Their operation is associated with the constant provision of reliability. FAS designers should also consider the system’s reliability when developing fire control matrices, tables, algorithms, or scenarios. All functions arising from an FAS control matrix should be implemented with a permissible reliability level, RDPN(t), prior to, as well as during, a fire. This should be assigned to the controls saved in the fire alarm control unit (FCP). This article presents the process by which high temperatures generated during a fire impact the reliability of FAS functioning. It was developed considering selected critical paths for a specific scenario and the control matrix for an FAS. Such assumptions make it possible to determine the impact of various temperatures generated during a fire on the reliability of an FAS. To this end, the authors reviewed that the waveform of the R(t) function changes for a given FAS over time, Δt, and then determined the fitness paths. The critical paths are located within the fire detection and suppression activation process, using FAS or fixed extinguishing devices (FEDs), and the paths were modeled with acceptable and unacceptable technical states. The last section of this article defines a model and graph for the operational process of a selected FAS, the analysis of which enables conclusions to be drawn that can be employed in the design and implementation stages.

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article’s goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components’ states in the neural network’s output cells. Based on the condition of the object’s constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement.

Among the most valuable types of renewable energy available today is wind energy. The reliability of WF systems must be regularly evaluated at every stage of their “life,” from design to operation, if a wind farm energy system is to be effective and function damage-free. Three key goals are presented in the article. The theory of fundamental quantities in reliability and maintenance analysis should be derived and explained first. Second, as a consequence of maintainability, theoretical correlations between reliability and mean time between failures (MTBF) are provided. The three-state theory of the WF procedure for operation presented in the research serves as the foundation for the analytical analysis of WF reliability. The time between failures is investigated as a function of maintainability, and the dependability of the WF under examination is assessed as a function of service life. The WF owner can make the best decisions to renew the WF and increase its reliability, energy, financial efficiency, etc. by being aware of the existing reliability of the WF system in use.

Under certain weather circumstances, wind farms might lose their operational integrity. The study describes the process for building a WPP (Wind Power Plant) expert knowledge base for the WPPES system. The article presents the creation of an expert knowledge base on wind farm equipment that is the basis for the functioning of an intelligent expert system. For this purpose, a functional and diagnostic model of wind farm equipment was presented and described. Functional diagnostic models of objects are the basis for obtaining diagnostic information about the study object (set of facts). The operating conditions of the wind farm equipment and their surroundings have been characterized and described. On this basis, admissible and boundary conditions for the functioning of the tested technical object were determined. The above information specifically supplements the diagnostic information set when building the set of facts and rules. An important part of the article is to describe the principles and conditions for developing a set of diagnostic rules for the knowledge base of an expert system diagnosing wind farm equipment. Building expert knowledge bases is an extremely complex process of transforming diagnostic object information sets into the form of knowledge that is required by an expert system. To this end, an analytical relationship was developed and described as the basis for building a set of inference rules for the examined object. The effectiveness of the developed expert knowledge base is presented in the aspect of its introduction to the expert system knowledge module.

The article deals with simulation tests on the reliability of the equipment of the wind farm WF in the operation process. The improvement, modernization, and introduction of new solutions that change the reliability, as well as the quality and conditions of use and operation of wind farm equipment, require testing. Based on these tests, it is possible to continuously evaluate the reliability of the equipment of WF. The issue of reliability assessment of wind farm equipment, for which intelligent systems, diagnostic systems DIAG, and Wind Power Plant Expert System (WPPES) are used to modernize the operation process, can only be tested in a simulative way. The topic of testing the reliability of complex technical objects is constantly developing in the literature. In this paper, it is assumed that the operation of wind farm equipment is described and modeled based on Markov processes. The adoption of this assumption justified the use of the Kolmogorov–Chapman equations to describe the developed model. Based on this equation, an analytically developed model of the wind farm operation process was described. The simulation analysis determines the reliability of the wind farm in terms of the availability factor Kg(t). The simulation tests are performed in two phases using the computer program LabView. In the first stage, the reliability value in the form of the readiness factor Kg(t) as a function of changes in the mean repair time value ranging {from 0.3 to 1.0} was investigated. In the second stage, the reliability value of WF devices was examined as a function of changes in the value of the average time between successive failures, ranging from 1000 to 3000 (h)}.

The evaluation and analysis of the procedures for determining the dependability of WF wind farm equipment employed in a few publications are this article’s main problems. The publications chosen for review specifically mention investigations into the dependability of WF wind farm machinery. The following topics were the authors’ main areas of analysis: description and review of the techniques used to represent how technical items operate and the selection of the weight of the theoretical ideas of reliability that were used to gauge the dependability of the wind farm equipment under study. The authors of the studied works set out to address a number of significant problems pertaining to the modernization of the management of the WF equipment renewal process. The subjects of the studied works suggest that the established models of the technical object’s operational process are particularly significant in both the theory and practice of the reliability of technical objects. Using Kolmogorov–Chapman equations, models of the WFD operating process that are based on the idea of Markov processes are very helpful for simulation studies.

One of the challenges associated with assessing critical systems is ensuring the appropriate quality of services. Supplying electricity is also one such service; however, the standards defining its assessment are not always consistent with the expectations of its consumers. This stems from the fact that the standards, which describe the quality of services associated with power supply, are based on a rather modest range of such parameters such as power supply continuity (interruption time), frequency, value, asymmetries, and time waveform shape (cf. EN 50160:2023). This article discusses the continuity quality of power supply (CQoPS), which takes into account numerous quality-related aspects, more than just the ones described in the standard. The method for determining CQoPS coefficients has been based on estimating uncertainty; therefore, it is devoid of such statistical evaluation disadvantages as the requirement for full knowledge of the system that is assessed. This paper also discusses an example calculation of one of the observations based on actual measurements of a renewable energy source (RES) power supply fed to metering systems and a result simulation depending on various observations.

The article presents the problems of diagnostics of low-power solar power plants with the use of the three-valued (3VL) state assessment {2, 1, 0}. The 3VL diagnostics is developed on the basis of two-valued diagnostics (2VL), and it is elaborated on. In the (3VL) diagnostics, the range of changes in the values of the signals from the 2VL logic was accepted for the serviceability condition: state {12VL}. This range of signal value changes for logic (3VL) was divided into two signal value change sub-ranges, which were assigned two status values in the logic (3VL): {23VL}—serviceability condition and {13VL}—incomplete serviceability condition. The state of failure for both logics applied of the valence of states is interpreted equally for the same changes in the values of diagnostic signals, the possible changes of which exceed the ranges of their permissible changes. The DIAG 2 intelligent system based on an artificial neural network was used in diagnostic tests. For this purpose, the article presents the structure, algorithm and rules of inference used in the DIAG intelligent diagnostic system. The diagnostic method used in the DIAG 2 system utilizes the method known from the literature to compare diagnostic signal vectors with the reference signal vectors assigned. The result of this vector analysis is the metric developed of the difference vector. The problem of signal analysis and comparison is carried out in the input cells of the neural network. In the output cells of the neural network, in turn, the classification of the states of the object’s elements is realized. Depending on the condition of the individual elements that make up the object, the method is able to indicate whether the elements are in working order, out of order or require quick repair/replacement.

The article reviews issues associated with the use of electronic fire alarm systems (FAS). They are operated in various environments and buildings with varying volumes. FAS have to function properly under different operating conditions associated with their operation, as well as power supply and information inflow. Due to their functions, i.e., ensuring the safety of people, vehicles, logistics bases, airports, etc., FAS have to exhibit an appropriately organized reliability structure associated with their implementation and power supply. Operational studies involving FAS operated in various facilities were conducted to this end. The authors determined damage and recovery time intensities. FAS reliability indicators were also determined. The article presents graphs associated with developing the energy balance for selected FAS. The graphs are consistent with the latest and applicable legal regulations. The next stage of the work related to this article was developing an FAS operation process model and conducting computer simulations in order to determine reliability indicators. Such an approach to the FAS operation process enables a rational selection of technical and organizational solutions aimed at guaranteeing reliability in the course of executing operational tasks associated with ensuring fire safety. FAS operational analysis, developing balance graphs and models, as well as the computer simulation, enabled inferring conclusions that might be useful to the process of engineering and operating such systems.