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Abstract Integrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind‐based GenCo equipped with a gas turbine and a power‐to‐gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day‐ahead electricity and natural gas markets and the real‐time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri‐level adaptive robust optimization model is introduced for the GenCo’s short‐term operational scheduling. At the first level, decisions related to the GenCo's participation in the day‐ahead electricity and natural gas markets are made. The second level deals with determining the worst‐case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model’s effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems.

Abstract Here, stochastic‐gradient‐based adaptive control algorithms have been discussed and employed for power quality enhancement in a Photovoltaics (PV) integrated distribution system. Least mean square (LMS), least mean fourth (LMF), sign‐error LMS, and ε‐normalised LMS (ε‐NLMS) have been implemented as control algorithms for the estimation of fundamental load current. The performances of these adaptive algorithms are compared under steady‐state and dynamic conditions under the non‐linear load conditions in a closed‐loop three‐phase system. The main aim of implementing these algorithms is reactive power compensation, power quality enhancement, and load balancing in a single‐stage three‐phase grid‐tied PV system. The hysteresis current control (HCC) technique is used to generate switching pulses for the three‐phase Distribution Static Power Compensator (DSTATCOM). An MPPT is also employed to ensure maximum power delivery from the solar PV array. PV integrated three‐phase single‐stage distribution system with adaptive control algorithms is implemented in MATLAB/Simulink environment as well as in experimental environment to achieve the goals per standard IEEE‐519.

Abstract With the development of ultra‐high‐voltage (UHV) and extra‐high‐voltage transmission lines in China, the transmission lines are gradually approaching the residential areas. The phenomenon of induced electric shock caused by electrostatic induction effect can cause dissatisfaction and complaints from nearby residents. In this paper, a full‐size steel‐framed residential test platform was constructed near a 1000‐kV UHV alternating current (AC) transmission line, the transient shock characteristics of human bodies and metal hangers in different scenarios was investigated, then the relationship between transient shock energy and subjective perception of the human body was discussed. In addition, the shielding effect of shielding wires for transient electric shock situations is analyzed. It was found that the worst‐case scenario occurred when the grounded human body came into contact with the insulated metal clothes hanger, even eliciting a noticeable sensation of pain. In addition, increasing metal shielding wire number has a significant effect on weakening induced electric shocks.

Abstract The variability of renewable energy sources due to weather patterns often leads to a mismatch between power generation and consumption within microgrids (MGs). This challenge is exacerbated when integrating bio‐renewable units, complicating stability maintenance in MGs. This research work introduces a novel solution to address this issue: a composite controller merging an integral terminal sliding mode controller with a recursive backstepping controller for direct current MGs (DCMGs). The proposed DCMG incorporates solar photovoltaic units, wind farms based on permanent magnet synchronous generators, proton exchange membrane fuel cells fuelled by hydrogen gas, an electrolyser, battery energy storage systems, and DC loads. First, a comprehensive mathematical model for the components within the DCMG is developed to design the proposed composite controller. This controller not only overcomes the inherent limitations and convergence issues of conventional SMCs but also ensures stable DC‐bus voltage and maintains power balance across various operational conditions. Moreover, a fuzzy logic‐based energy management system is introduced to regulate power flow, considering factors like battery state of charge and renewable energy sources' total power output. The control Lyapunov function confirms the DCMG system's asymptotic stability. Finally, the proposed controller's effectiveness is validated through simulations on both MATLAB/Simulink and Arduino Mega 2650 processor‐in‐the‐loop platforms under various operational conditions. In both platforms, the proposed controller surpasses an existing controller in terms of settling time, overshoot, and tracking error of the DC‐bus voltage.

Abstract Protection of synchronous generators (SGs) against internal faults, such as stator earth fault (SEF) and turn‐to‐turn fault (TTF), is crucial for ensuring the stability and security of the power system. This paper presents a phase domain model for simulating SEF and TTF in SGs, requiring only nameplate data and avoiding the need for complex geometric data or lengthy simulations typical of FEM models. The stator winding is divided into three sections, allowing for the calculation of magnetic field distribution in both healthy and faulty conditions. The model is capable of simulating short‐circuit turns at various locations within the stator winding with high accuracy and speed. The dynamic response of the generator is also incorporated into the model. The model's accuracy is validated through comparison with results from multiphysics simulation software. Furthermore, this study addresses the limitations of conventional protection methods in detecting TTF and proposes a novel, simple, fast, and accurate protection logic that can be implemented in digital protection relays and is effective across a wide range of TTF scenarios.

Abstract The lightweight of modular multilevel converter (MMC) and the DC faults ride‐through ability are main challenges for MMC‐high voltage direct current (HVDC) transmission systems. By introducing the concept of time‐division multiplexing, an arm multiplexing MMC (AM‐MMC) topology with high utilization of submodules is presented to reduce the weight and volume of MMC. In order to block the DC side fault current, this paper proposes a novel submodule in AM‐MMC, instead of using full‐bridge submodules. The proposed recombined half‐bridge submodules of AM‐MMC (RHAM‐MMC) contains four half‐bridge submodules and an IGBT with reverse parallel diodes. The topology and operating principle of RHAM‐MMC are introduced in detail. The time‐division multiplexing of middle arms between upper and lower arms is achieved by introducing arm selection switches. Thus, a new type of arm switch and switching method is designed based on the switch state. The DC faults ride‐through strategy is carried out based on its DC fault characteristic analysis. In addition, the economy analysis is conducted on the switching loss and operating loss of RHAM‐MMC. Compared with the fault ride‐through capability of other sub‐modules (SMs), RHAM‐MMC performs better in terms of investment cost and device losses. The simulation results based on MATLAB/Simulink reveal that RHAM‐MMC can achieve the DC side fault ride‐through and show effectiveness of the DC fault ride‐through control strategy.

Abstract Large‐scale wildfires can significantly reduce the air gap insulation resistance of high‐voltage transmission lines and cause chain tripping incidents. To assess the resilience of the power transmission system during wildfire, this paper proposes a resilience assessment framework for transmission system that considers the entire process of wildfire disaster. Firstly, a wildfire spread model, considering multiple influencing factors, is developed based on the cellular automaton. Based on the air gap breakdown mechanism during wildfires, the trip‐out probability of transmission lines is calculated, and various failure scenarios are obtained by using the Monte Carlo sampling. Secondly, considering the geographical location of failures, maintenance personnel schedules and restoration time, a power transmission system restoration model is established. Thus, a resilience assessment method for power transmission system during wildfire disasters is proposed. Finally, IEEE RTS‐79 transmission system is taken as an example to demonstrate the effectiveness of the proposed resilience assessment method. The results show that the proposed method can effectively calculate the wildfire spread tendency and transmission line's trip‐out probability. Furthermore, three typical resilience improvement measures are quantitatively analysed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfire disasters.

Abstract Recently, distributed generators (DGs) have been widely integrated into distribution network, so that the distribution network is gradually transforming into an active distribution network (ADN). Due to the influence of meteorological conditions, the output of DGs has high uncertainty. At the same time, considering the increasing variety of loads in ADNs, the uncertainty of load demand of user side is also increasing. In order to fully consider the uncertainty of measurement and quantitatively evaluate the operational status, this paper proposes a steady‐state analysis method for ADNs under uncertain conditions. Firstly, this paper proposes a steady‐state analysis method including power flow analysis model and evaluation indicators for the operation status from the perspectives of node and network. Secondly, the uncertainty factors are elaborated from three aspects: sources, impact on evaluation index and impact on scheduling. The evaluation indicators considering uncertain conditions, the impact on system security and scheduling of network are further discussed. Finally, through the simulation analysis of the modified IEEE 33‐node test system, the effectiveness of the proposed method is verified.

Abstract With the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

Abstract This article proposes a multi‐objective and multi‐stage low‐carbon planning approach for park integrated energy systems (PIES) considering the impacts of random outages from the superior electrical grid. This approach incorporates optimal multi‐stage construction sequencing and stepped carbon emission trading to leverage the economic and low‐carbon benefits of long‐term planning. First, the islanding modes of PIES are described using four random variables: island type, duration, start time, and typical day of occurrence, from which islanding scenarios are generated based on scenario tree. Next, a multi‐objective planning model that considers both economics and reliability is constructed, with the objectives of minimizing the total lifecycle planning cost and the expected economic loss during islanding. The improved Normalized Normal Constraint (NNC) method is proposed to solve the multi‐objective planning problem. Then, the fuzzy membership function is used to determine the optimal compromise solution, resulting in a planning scheme that balances economic efficiency and supply reliability. Finally, simulations indicate that, at the cost of a slight increase in planning expenses, the proposed model significantly reduces the loss costs under islanding modes compared with single‐objective economic‐focused planning. Additionally, the improved NNC method can achieve a more uniform Pareto frontier compared with the conventional NNC method.

Abstract Power systems are highly complex, large‐scale engineering systems subject to many uncertainties, which makes accurate mathematical modeling challenging. This article introduces a novel centralized dynamic state estimator designed specifically for power systems where some component models are missing. Including the available dynamic evolution equations, algebraic network equations, and phasor measurements, the least squares criterion is applied to estimate all dynamic and algebraic states recursively. The approach generalizes the iterated extended Kalman filter and does not require static network observability, relying on the network topology and parameters. Furthermore, a topological criterion is established for placing phasor measurement units (PMUs), termed topological estimability, which guarantees the uniqueness of the solution. A numerical study evaluates the performance under short circuits in the network and load changes and shows superior tracking performance compared to robust procedures from the literature with computational times in accordance with the typical PMU sampling rates.

Abstract The integration of super large‐scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two‐stage collaborative planning of points of common coupling siting and transmission network expansion for super large‐scale offshore wind clusters, based on a multi‐voltage‐level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two‐stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30‐bus system and a 65‐bus system.

Abstract The grid‐connection of high‐permeability new energy through voltage‐source converters (VSCs) brings new oscillation risks to the power system, which seriously threatens the stable operation of the system. Therefore, an evaluating method based on modal analysis is proposed to investigate the power oscillation characteristics in a multi‐VSC grid‐connected system. Initially, based on the analogy method, the output admittance model of voltage‐source controlled VSCs (VC‐VSCs) and current‐source controlled VSCs (CC‐VSCs) is established. Subsequently, the output admittance model of a multi‐VSC grid‐connected system is constructed. Then, the modal analysis method is introduced to investigate the power oscillation characteristics of VC‐VSCs and CC‐VSCs, and the influence factors of power oscillation and its variation law are analysed. Next, a sensitivity analysis is also employed, which helps to identify the origin of each oscillation mode and determine the contribution of each component to the power oscillation. Finally, simulation and experiment are carried out to verify the effectiveness of the above analysis method.

Abstract This article introduces innovative protection strategies, including cooperative protection, for power transmission grids amidst a significant shift towards renewable energy sources (RES) such as wind and solar power, as well as inverter‐based resources (IBRs). The method employs a global consensus algorithm to achieve cooperative protection efficiently. This scheme leverages consensus protocols to dynamically oversee distance relay decisions, ensuring efficient fault detection and localization. The decentralized nature of the proposed method enhances robustness and security, while its high‐speed operation is ensured through non‐iterative global consensus algorithms, which provide rapid fault detection and localization crucial for real‐time protection. By incorporating virtual leaders and leveraging existing communication infrastructure, the method achieves superior selectivity in identifying faulty lines, enhancing the reliability and stability of power transmission grids with high‐RES penetration. Notably, the method does not require learning and training processes, making it adaptable to varying power system topologies without the need for extensive retraining or adaptation periods. The proposed methodology enables simultaneous participation in multiple protection zones by establishing interaction rules between agents. Virtual leaders simplify the selection of protection areas, enhancing scalability and fault localization. Simulation results conducted on the IEEE 39‐bus test system validate the effectiveness of the proposed method.

Abstract The escalating global population and energy demands underscore the critical role of renewable energy sources, particularly solar power, in mitigating environmental degradation caused by traditional fossil fuels. This paper emphasizes the advantages of solar energy, especially photovoltaic (PV) systems, which have become pivotal in hybrid energy systems. However, accurate modelling and identification of PV cell parameters pose challenges, prompting the adoption of meta‐heuristic optimization algorithms. This work explores the limitations of existing algorithms and introduces a novel approach, the bio‐dynamics grasshopper optimization algorithm (BDGOA). The BDGOA addresses deficiencies in both exploration and exploitation phases, exhibiting exceptional convergence speed and efficiency. The algorithm's simplicity, achieved through the implementation of an elimination phase and controlled search space, enhances its performance without intricate calculations. The study evaluates the BDGOA by applying it to identify unknown parameters of five solar modules. The algorithm's effectiveness is demonstrated through the extraction of parameters for RTC France, PWP201, SM55, KC200GT, and SW255 models, validated against experimental data under diverse conditions. The paper concludes with insights into the impact of radiation and temperature on module parameters. The subsequent sections of the paper delve into the intricacies of the PV cell and module model, articulate the formulation of the proposed algorithm, present simulations, and analyse the obtained results. The BDGOA emerges as a promising solution, overcoming the limitations of existing algorithms and contributing significantly to the advancement of accurate and efficient PV cell parameter identification, thereby propelling progress towards a sustainable energy future.

Abstract Focusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding the precise allocation of funds. Considering that the residents' intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents' psychological risk perception and risk‐taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post‐disaster collaborative dispatch of electricity‐gas‐water systems is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi‐source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post‐disaster restoration stage.

Abstract The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Abstract The complexity of power quality (PQ) concerns is intensifying in tandem with the proliferation of inverter‐based renewable energy systems. The integration of power electronic devices within the distribution network exacerbates the complexity and introduces greater temporal variability to signal components. This paper introduces an advanced, online optimization technique for the decomposition and identification of PQ disturbances (PQDs). Initially, an improved variational mode decomposition (IVMD) method is presented, leveraging an energy ratio criterion for precise decomposition of concurrent PQDs. Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. The OSVM strategically employs distinct kernel functions to augment the discriminability of the feature set. The synergy of IVMD and OSVM enables the detection of multiple PQDs, remarkably even with a minimal amount of training data. A series of experiments have been conducted to validate the effectiveness of the proposed methodology. The results corroborate that the formulated framework exhibits robust learning capabilities and a high degree of resistance to noise interference. Moreover, the hardware platform experiments prove that the proposed method has a satisfactory real‐time performance for its practicability.

Abstract One of the primary challenges faced by generation companies (GenCos), which operate multiple generation units within the electricity market, is the determination of the optimal bid price for these units to maximize profit. This paper proposes a novel approach to ascertain the optimal bid price direction for GenCos by leveraging the gradient vector of the profit function within the constraints of the electricity market. First, the Jacobian matrix of unit profits is computed using the electricity market structural decomposition method. This matrix highlights how the profit of generation units is affected by market input parameters, including the bid prices of the units. Then, the gradient vector of the GenCos' profit function and the optimal bid price direction are derived from the Jacobian matrix. The methodology is applied to a 24‐bus IEEE network, with results validated against those from a simulation method to confirm the efficacy of the proposed approach. The simulation results show that the highest and lowest profit changes with a step increase of 0.1$/MWh are observed for GenCo 4 and GenCo 6 with values of 60.28 and 2.20 $/h, respectively. The proposed approach can be effective in the changes of bid direction of the units of a GenCo to achieve the highest possible profit.

Abstract The introduction of cloud‐native technology has significantly changed the architecture of applications and the mechanism for the collaborative operation of components in power distribution edge computing terminals (PDECT). To develop an effective quantitative analysis tool for PDECT performance, the composition and characteristics of cloud‐native PDECT are studied, and the modelling and simulation of cloud‐native PDECT are proposed. Subsequently, modelling is implemented through the simulation software CloudSim, achieving the simulation of microservices, containers, declarative configuration, and container orchestration with the consideration of power distribution scenarios. Then, by the proposed simulation scenario module, various elements of the power distribution scenarios can be self‐defined. Finally, by demonstrating the principles and implementation mechanisms of the proposed modelling method and simulation tool, and comparing simulation results for different service time ranges, access devices, resource configurations of PDECT, request occurrence rates, and resource scheduling strategies, the validity and effectiveness of the proposed modelling method and simulation tool are verified.

Abstract Increasing the use of renewable energy in microgrids (MGs) offers environmental and economic benefits. However, the unpredictable and intermittent nature of available resources poses challenges for optimal MG scheduling. Hybrid AC–DC microgrids provide a solution, seamlessly integrating renewables while reducing energy losses and improving power grid reliability. Additionally, incentive‐based demand response programs promote flexible energy consumption, further mitigating the variability of renewable generation and enhancing grid stability. This paper investigates the challenges and potential of high renewable penetration in hybrid AC–DC MGs, analysing the role of demand response programs in system optimization. The microgrid's energy management is modelled using MILP, while a Stackelberg game represents the demand response program. These models are integrated to optimize energy management and demand response jointly. Simulations demonstrate the cost‐saving benefits of this integrated framework, achieved through coordinated flexible resource scheduling and incentive‐based demand response programming.

Abstract This paper presents an approximate power flow model‐based forecasting‐aided state estimation estimator for power distribution networks subject to naive forecasting methods and nonlinear filtering processes. To this end, this estimator designs a voltage perturbation vector around the priori‐determined nominal value as the dynamic state variable, which enables more detailed depictions of voltage changes. Then, a state transition model incorporating nodal power variation is derived from the approximate power injection model. The constant state transition matrix working on power variations only consists of nodal impedance, which reduces the extensive parameter tuning effort when facing different estimation tasks. Furthermore, an approximate branch power flow observation equation is proposed to improve the filtering efficiency. The observation matrix with branch admittance information presents the linear filtering relationship between power flow measurements and forecasted states, omitting the complex iterative updates of the Jacobian matrix for nonlinear measurements. Finally, the overall estimated voltage state at each time sample is entirely obtained by combining the filtered voltage perturbation vector with the priori‐determined nominal value. Numerical simulation comparisons on a symmetric balanced 56‐node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness under normal and abnormal conditions.

Abstract The correct generator circuit breaker (GCB) dimensioning is essential for the safe and reliable operation of a power plant or generation system. The dimensioning is usually based on standardized calculation methods according to standards (IEC standard 60909‐0, IEC/IEEE standard 62271‐37‐013, IEEE Std C37), often supplemented by selected transient calculations. A non‐systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short‐circuit current components of the generator and are relevant for the dimensioning of the generator circuit‐breaker: short‐circuit angle, operating point, impedance ratios, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Special attention was paid to the effect of the switching and fault arc, which were modelled as a constant arc voltage, and its effect on the short‐circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short‐circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short‐circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

Abstract This paper presents a prediction‐based algorithm designed to address out‐of‐step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi‐machine power system is evaluated using the equal‐area criterion (EAC). The proposed algorithm calculates the characteristics of the P‐δ curves within the EAC framework after a large disturbance. The critical P‐δ trace is determined by analysing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. Furthermore, a complementary strategy is proposed to predict the OOS condition in integrated inverter‐based power systems. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73‐bus IEEE test power system.

Abstract The challenges facing active distribution networks have highlighted the position of the distribution system state estimation (DSSE) process in the distribution management systems as its most important function. Here, regarding the extensive scale of distribution networks and the weaknesses of centralized methods, the decentralized implementation of the DSSE process has received considerable attention. However, predefined network partitioning is supposed in previous works and zone size effects on the performance of the DSSE process have not been assessed. In response, a method for finding the optimal number of network zones and their size is proposed here. For this purpose, initially, an algorithm is used to partition the network into all possible configurations with different sizes. Subsequently, performance metrics affected by zone sizes, such as execution time, accuracy of the DSSE results, and reliability in achieving the results at the control centre, are modelled. Finally, by applying the decentralized DSSE method across all partitioning scenarios and calculating performance metrics, the most efficient and cost‐effective partitioning scenario can be identified. The performance of the proposed method is evaluated using the modified 77‐bus UK distribution network as an active test case, and the findings are subsequently presented and analysed.

Abstract This article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power‐swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series‐compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power‐swing conditions of the power system.

Abstract This paper addresses the challenge of maximizing power capture from new energy sources, including coal, wind, solar, and hydroelectric power, which often lack sufficient inertia support. This deficiency can lead to frequency instability and cascading failures within the power system. A cooperative optimization model for the start‐up of multiple power supplies, designed to enhance the integration of new energy sources while maintaining system stability is proposed. The model incorporates primary frequency modulation and the intrinsic inertia support capabilities of self‐synchronous voltage source field stations, considering dynamic frequency constraints. Additionally, it employs new energy units with primary frequency modulation to provide inertia support during curtailment, particularly when conventional units cannot meet frequency standards due to existing constraints. Extensive simulations and comparative analyses demonstrate that the proposed model improves new energy utilization by up to 37.5% and reduces operational costs by approximately 16%, enhancing overall operational efficiency in high energy consumption scenarios.

Abstract The recognition of the transient dominant instability mode is of great significance for rapidly and accurately formulating transient emergency decisions in power systems. In response to the challenge of accurately distinguishing between angle instability and voltage instability, which are coupled in actual power grids, this paper explores the mapping relationship between simulation data and the stable state of the system, as well as the dominant instability mode. The method enables real‐time identification of the dominant instability mode, which bypasses complex physical mechanisms. Firstly, spatio‐temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. Next, a multihead attention mechanism is introduced to enhance sensitivity to important time steps in the sequence data. Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. Using the IEEE 10‐machine and 39‐node system as an example for simulation, the results validate that the proposed method exhibits significant advantages in terms of accuracy and applicability compared with other machine learning methods.

Abstract In an era characterized by the rapid proliferation of distributed flexible resources (DFRs), the development of customized energy management and regulation strategies has attracted significant interest from the field. The inherent geographical dispersion and unpredictability of these resources, however, pose substantial barriers to their effective and computationally tractable regulation. To address these impediments, this paper proposes a deep reinforcement learning‐based distributed resource energy management strategy, taking into account the inherent physical and structural constraints of the distribution network. This proposed strategy is modelled as a sequential decision‐making framework with a Markov decision process, informed by physical states and external information. In particular, targeting the community energy management system for critical public infrastructure and community holistic benefits maximization, the proposed approach proficiently adapts to fluctuations in resource variability and fluctuating market prices, ensuring intelligent regulation of distributed flexible resources. Simulation and empirical analysis demonstrate that the proposed deep reinforcement learning‐based strategy can improve the economic benefits and decision‐making efficiency of distributed flexible resource regulation while ensuring the security of distribution network power flow.

Abstract Electric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.

Abstract With the development of the Internet of Things (IoT) in power distribution and the advancement of energy information integration technologies, the explosive growth in network data volume caused by massive terminal devices connecting to the power distribution network has become a significant challenge. Multi‐terminal collaborative computing is a key approach to addressing issues such as high latency and high energy consumption. In this article, fog computing is introduced into the computing network of the power distribution system, and a cloud‐fog‐edge collaborative computing architecture for intelligent power distribution networks is proposed. Within this framework, an improved weighted K‐means method based on information entropy theory is presented for node partitioning. Subsequently, an improved multi‐objective particle swarm optimization algorithm (MWM‐MOPSO) is employed to solve the task resource allocation problem. Finally, the effectiveness of the proposed architecture and allocation strategy is validated through simulations on the OPNET and PureEdgeSim platforms. The results demonstrate that, compared to traditional cloud‐edge service architectures, the proposed architecture and task offloading scheme achieve better performance in terms of processing latency and energy consumption.

Abstract In this paper, the study of the homogeneous resistivity and two‐layer soil influences on the performance of the electrodes of grounding systems subjected to lightning strikes is carried out considering soil ionization. The study is done on the behaviour of the ground electrodes while the resistivity of the soil is altered from small to high value in the case of homogeneous soil, while the study is obtained on two‐layer soils in the case of diverse values of the reflection factor. In the suggested algorithm for two‐layer soils, each layer has its resistivity and dielectric constant, where the dielectric constant and soil resistivity depend on the lightning frequency. The field resulting from lightning strikes, which causes soil ionization, has been studied, and the results have been used to understand the behaviour of ground rods under the influence of lightning strikes. The credibility of the results has been strengthened by comparing it with what others have obtained recently. The article's novelty can be summarized in the investigation of the performance of the electrodes of grounding grids that are installed in high soil resistivity of uniform and two‐layer soils containing the impacts of soil ionization, lightning frequency, soil resistivity, and permittivity variations.

The functioning of deeply integrated technological systems in the power industry depends on the quality of power supply systems. Reliable power supply requires minimizing emergencies at power plants and substations. Reducing the accident rate is possible by switching to the mainte-nance and repair of high-voltage equipment based on its actual technical state. Machine learning methods make it possible to assess the actual technical state of various equipment types based on an aggregated analysis of many parameters, in contrast to methods based on comparing pa-rameters with boundary values without considering their mutual influence. Therefore, machine learning can be an effective tool for decision support systems for equipment diagnostics. The opacity of the models and the lack of interpretability of their recommendations significantly complicate industry implementation. The purpose of the study is to develop a method for in-creasing the interpretability of machine learning models for assessing the technical state of equipment. A power transformers’ oil dataset, decision tree ensembles and the Shapley additive explanations were used. The novelty of this study is the modification of the additive Shapley additive explanations, aimed at increasing the information content of the visualization of ma-chine learning model output interpretation. The most significant results are the substantiation of the applicability of explainable artificial intelligence for the assessment of the technical state of high-voltage equipment. In addition, for the first time in this problem, a Light gradient boosting method was applied. The proposed method allows for increasing the validity of the technical state assessment of high-voltage equipment using machine learning.

The article’s aim is to create a method of reducing the influence on the surface eddy current probes output signal of a number of interfering parameters without their elimination, to ensure its stability, which is achieved as a result of computer search of design and operating values of probes Taguchi-parameters at the stage of their design. To achieve the goal the following tasks were carried out: software for calculating the output signal of surface probes, which is based on the electrodynamic model of the measurement process for the test object in the form of a con-ductive plate of infinite overall dimensions and finite thickness; using the Taguchi method, a special design of experiments with the use of orthogonal arrays is constructed, the fulfillment of which makes it possible to determine the influence of each parameter and their complex nonlin-ear interaction on the output signal of the probe; by integrating the Taguchi method with com-puter numerical simulation, the optimal controllable parameters of the probe were determined, minimizing the signal changes caused by noise factors. The most important research result is the method creation of providing the signals probes stability and reducing the influence of noise pa-rameters on them simultaneously without their elimination. The method effectiveness is demon-strated on the example of designing a thickness gauge, which allowed computer search for a set of its optimal parameters to maximize the signal-to-noise ratio, thereby reducing the signal varia-bility. The significance of the obtained results lies in the creation of theoretical and practical ap-proaches to the design of surface eddy current probes with new properties of interfering factors suppression, which provide the best conditions for reliable diagnostics of power equipment ob-jects.

The use of machine learning methods to control heat supply systems is a pressing issue worldwide. The aim of the work is to develop methods for intellectualizing the operating modes of individual heat supply units (IHU) to improve their efficiency and reliability. To achieve this goal, the following tasks were solved: creating and debugging methods for diagnosing IH operating modes; using the cluster analysis method, in particular the K-means algorithm, to identify pre-emergency situations at an early stage; analyzing the relationship between outdoor air temperature data and the pressures of direct and return network water in IHU operating modes using Novosibirsk as an example. The most important results of the work include dividing the measured parameters into five clusters, each of which characterizes a certain IHS operating mode. This was confirmed by the "Elbow Method", which determined the optimal number, which made it possible to significantly improve the forecasting of emergency modes. Studies have shown that a sharp increase in outdoor temperature leads to an increase in the pressure of direct network water, which can cause accelerated wear of heating networks due to the peculiarities of weather-dependent automation regulation. Introducing additional parameters into the initial data, such as the service life of heating networks and weather conditions, can improve the accuracy of forecasts. The significance of the obtained results lies in the possibility of early detection of emergency and pre-emergency modes of IHU operation, which helps prevent accidents and reduce repair and maintenance costs.

The purpose of the work is to build the rational system for regulating technological parameters of the single-screw extruder using the frequency-regulated electric drive with limited use of sensors. This goal is achieved by the way of solving the following problems: development the method for controlling the technological parameters of the extruder with a controllable electric drive; development of a structural diagram of a model for estimating the speed of rotation a rotor of the engine without the use of velocity-type transducers; development of a functional diagram of the vector frequency-regulated electric drive with an observer that is built on the basis of a voltage equation corresponding to the main magnetic flux; development of a simulation model of the system of adjustable electric drive of the extruder and carrying out simulations with using the Matlab software package in the environment of Simulink. The most significant results of the researches are: the proposed algorithm for controlling the technological parameters (dynamic viscosity and pressure) by the way of calculating of the electrical power parameters of the adjustable electric drive of the extruder without the use velocity-type transducers; the functional diagram of the vector frequency-regulated electric drive of the single-screw extruder with the observer that was built on the basis of balance of the reactive capacity provides the necessary values of the technological parameters of the extruder. Changes in dynamic viscosity and pressure in the material, provides flexible adjustment without the use of speed sensors and rational use of electricity.

Ukraine Abstract. The goal of the work is to increase the yield of methane in biogas plants due to the joint methane fermentation of cattle manure with apple pomace. To achieve this goal, the following tasks were solved: the yield of biogas from cattle manure was determined with the addition of apple pomace to the substrate and fermentation in psychrophilic and mesophilic temperature conditions of the biogas plant. Based on the obtained experimental data of fermentation at temperatures of 22±1°C and 36±1°C of the fermenter, the biogas yield was assessed and the rational temperature regime for the operation of the biogas plant was determined. The studies were carried out on a laboratory biogas plant, consisting of a fermenter and a wet-type gasholder. A glass container with a total volume of 3 dm3 was used as a fermenter. The novelty of the chosen topic lies in the fact that the maximum methane output rate was determined under periodic loading of the fermenter, which for the psychrophilic temperature regime is 0.0033 m3/(kg DOM∙day), and in the mesophilic temperature regime mode 4 times more - 0.012 m3/(kg DOM∙day). The significance of the results lies in the fact that based on the results of the dynamics of methane output during periodic loading of the fermenter, it is possible to predict a constant methane output in industrial biogas plants that operate in a quasi-continuous mode, which will be equal to the maximum methane output during periodic loading of the fermenter.

Increasing the efficiency of using fossil fuels at thermal power plants is an urgent task. This problem arises most acutely during the operation of pulverized coal power boilers using flare fuel combustion technologies. The purpose of this work is an experimental and computational study and justification of the technology of low-temperature combustion of coal in a combustion chamber with a vertical precessing vortex and the creation of a prototype combustion chamber for a power steam boiler. To achieve this goal, a study of the process of two-stage coal combustion was carried out, when the first stage of coal combustion is carried out in a pre-furnace with a fluidized bed, and the after-combustion of the products of incomplete combustion of coal is carried out with gas illumination in an after-combustion chamber with a vertical precessing vortex. The relationship between the main design parameters of the combustion chamber and the main operating characteristics of the diffusion vortex has been determined. The most important results are: development and experimental validation of the parameters of the combustion chamber with a vertical precessing vortex; determination of optimal operating modes of the furnace when burning coal and combined combustion of coal and gas; creation of a prototype furnace for a power steam boiler with natural circulation, operating on Kansk-Achinsk coal. The significance of the results obtained lies in the possibility of their use in the design of new or modernization of existing power boilers in order to increase the efficiency, reliability and environmental safety of operation.

The aim of the work is to develop the uniaxial ferrofluid sensor suitable for use either as an ac-celerometer for low-frequency vibrations or as a ballistic device or seismic sensor for shock loads. The goal is achieved by solving the following problems: development of a magnetic sus-pension system with a linear axial gradient of the magnetic field strength, calculation of the vis-cous friction force of the magnetic fluid filling the coaxial layer between the magnetic cylinder and the non-magnetic body wall, manufacturing of the sensor and its static and dynamic tests. The most significant result of the work is the experimental confirmation of the linearity of the electromechanical system of the sensor, corresponding to the model representations of a linear dissipative oscillating system with one degree of freedom. The significance of the obtained re-sults lies in the appearance of cheap and simple linear inertial sensors. The principle of opera-tion of the sensor is based on the registration of the motion of an inert mass deviating from the equilibrium position under the action of an external force to be measured. The inert mass, con-sisting of ring permanent magnets, levitates in a coaxial layer of magneto-fluid lubricant. The sensor, depending on its parameters, can measure either quasi-static force, or force impulse, or coordinate displacement, which is in demand in monitoring systems for critical structures and buildings, as well as in navigation systems for vehicles operating under conditions of small, slowly changing accelerations (microgravity).

The aim of the study is to develop a methodological approach to assessing the incremental effi-ciency of electricity generation at CHPPs (specific heat consumption for changing electric power of turbine) in operating modes, taking into account both energy and environmental indicators. To achieve this goal, the following tasks were solved: the methodology, based on mathematical models of cogeneration steam turbines, both integral and differential indicators of heat rate per unit of electricity generation increment, was justified; a comprehensive generalization of the cal-culation data on the turbine T-50-12.8 type was performed; a quantitative assessment of the in-tegral electricity generation and greenhouse gas emissions parameters was made at various loads and its comparison with the corresponding indicators in condensation mode. The most signifi-cant results are the following: generalized dependences of the integral specific heat consumption per unit of increase in electricity generation and carbon footprint were calculated for a wide range of changes in operating conditions; boundary conditions were established under which obtaining these parameters by opening the sliding grid of low-pressure section turns out to be energetically and ecologically feasible; it was determined that when increasing the capacity without bypassing the delivery water, the carbon footprint increment with an increase in the ca-pacity of cogeneration turbines by opening the sliding grid will be less than for condensing tur-bines. The significance of the results obtained lies in their applicability for solving problems of ensuring maximum efficiency during involving cogeneration turbines in regulating electric load schedules in different boundary conditions.

The article examines the features of the combustion process of hydrogen-containing gas (syn-gas) in the working cylinder of a spark-ignition internal combustion engine. The main objective of the study is to evaluate the heat release parameters of syngas. To achieve this goal, an exper-imental investigation was conducted to assess the nature of syngas combustion and establish its regularity. The most significant result is the derivation of dependencies for determining the cur-rent value of the combustion characteristic index m and the combustion duration φz in Professor I.I.Vibe's heat release model for spark-ignition engines operating on syngas, with an air excess ratio α ranging from 1.0 to 2.2 and hydrogen content in the fuel composition varying between 30% and 100% by volume. The significance of the obtained results lies in the establishment of the variability of the combustion index m (ranging from 1.6 to 5.5) in Professor I.I.Vibe's semi-empirical heat release model, which more closely corresponds to the actual heat release law ob-served in experimental studies. The proposed dependencies for determining m and φz allow for accounting the specific features of the syngas combustion process in spark-ignition engines, thereby significantly improving the accuracy of determining the indicated pressure in the work-ing cylinder (the relative root-mean-square error does not exceed 4.5%), which, in turn, enhanc-es the accuracy of assessing the engine's energy and economic parameters. The results of this study can be applied in the design and construction of new spark-ignition engines running on alternative fuel.

The aim of the work is to obtain practical results for the electrical machines multiparametric de-sign application, to conduct a comparative analysis of the parametric and multiparametric design results of a turbogenerator and its elements when the load changes; to confirm the prospects the multiparametric design application. Currently, it is possible to use mathematical models when designing, and use parametric synthesis to optimize one specific parameter, which in certain cas-es turns out to be sufficient. The peculiarity of electric machines calculation and design is the necessity of simultaneous checking of all interrelated parameters (mechanical, electromagnetic, thermal) with any changes. The structure design with simultaneous control of several variables cannot be performed using one stage of parametric design; additional calculations are necessary. In this case, multi-parameter design is necessary, but it requires longer preparation for design work and higher qualifications of personnel. Therefore, it is necessary to understand in which cases it is advisable to switch from parametric to multiparametric design. The work goal set is achieved by comparing the results of a turbogenerator, stator core and busbar designing using parametric and multiparametric design. The most important is to obtain the results of comparison of a complex electromechanical system parametric and multiparametric design (turbogenerator, its individual unit and element), including when the parameters of various physical processes change, performing a comparison of the obtained results, confirming the feasibility of carrying out multiparametric design. The significance of the obtained results is that practical examples of the multiparameter modeling use are shown. Using multiparametric design, not only a busbar refined form with increased repair and operational reliability was obtained, but the proposed so-lution economic advantages were also indicated.

The article aims to contribute to the ongoing study of damage to steam turbine rotors resulting from asynchronous connection of electric generators to the unified power system. At the present time, the assessment of the residual life of steam turbine equipment is based on the study of the degradation of the mechanical properties of steels and the analysis of the thermal stress state. The aim of this work was to determine the impact of such vibrations on the mechanical proper-ties of rotors and their damage. This goal was achieved by solving the following tasks: develop-ing a 3D model of the K-1000-60/3000 LMZ turbine unit shaft based on design documentation; calculating the stress-strain state of the shaft resulting from asynchronous connections using fi-nite element analysis; and evaluating the level of metal damage in the rotors due to torsional vi-brations. The developed mathematical model used a classical approach, replacing the working blades and bandage fastenings with concentrated masses and moments of inertia. The most im-portant results are the calculated rotor damages that occur due to asynchronous connection of the turbogenerator to the unified power system with a synchronization angle of 30°. The greatest damage to the rotor metal was found in the shaft section between the steam turbine and the elec-tric generator. The significance of the results obtained is that the use of this data will improve methods for assessing the remaining life of turbogenerators. This, in turn, will improve the relia-bility and safety of power plant operations.

The control system of a bivalent heat pump (BHP), using as low potential heat (LPH) sources both the heat of the return water of the network and the heat of the outside air, is considered. The aim of the work is to determine the parameters of the thermodynamic cycle of the heat pump, which would ensure the operation of the heat pump at variable temperatures and flow rates of the refrigerant in the load. The set goal is achieved by solving the following tasks: analy-sis of the methods of synthesis of systems at variable load, analysis of the operation of the sys-tem at random disturbances, development of the heat pump automatic control system (ACS). The main result of the work is: the scheme of the heat pump, which can work with variable pressures of the evaporator and the gas cooler, as well as the technical solution, in which the en-thalpy difference at the evaporator remains constant regardless of the outside air temperatures Significance of the obtained results consists in creation of the BTN scheme, which allows to provide both qualitative and qualitative-quantitative laws of regulation of thermal regime of a building at increase of SOR of a heat pump, thanks to rational choice of a temperature schedule of regulation. Local storage using electrochemical accumulators at the required capacity would be very expensive and would make the entire installation unprofitable. The only reasonable so-lution is interaction with the grid, which requires, in addition to the technical means of interfac-ing, the presence of appropriate regulations governing the transfer of electricity generated by the local facility to the grid, especially if the generation will be carried out throughout the year.

The object of this study is the process of generating an appropriate response by an intelligent agent when detecting and tracking an underwater electrical cable using a decision support system. The goal of the work is to develop the architecture of a decision support system for diagnosing underwater electrical cables and the main algorithms for its operation. To achieve the research goal, intelligent agents with a complex IPK architecture, which includes information, preferences (rules), and knowledge, were used. The combination of the structure of intelligent agents and the advantages of hierarchical knowledge bases allowed for the natural language representation of knowledge. The functional core of the system consists of four main agents, one of which facilitates the interaction between the user and the surrounding environment. To address the uncertainty in the cable's position on the seabed, the capabilities of fuzzy sets, describing its feature space with membership functions, were employed. The most significant results involve planning for the detection and tracking of underwater electrical cables, taking into account past decision-making experiences in similar cases and adapting them to the current situation through case-based reasoning. The importance of the obtained results lies in providing the decision-maker with a possible option for deploying an underwater vehicle based on accepted logic and rules for detecting underwater electrical cables. Further research is focused on implementing automatic target selection for intervention and developing a method for automatic modification of reasoning methods, preference rule bases, and operational knowledge.

The work focuses on wireless power transfer for electric vehicle charging stations. The aim of the study is to analyse the processes in the system of wireless magnetic resonance transmission of electrical energy using magnetic fields and to develop approaches to improve the efficiency of such systems. The set objectives were achieved by solving the following problems: the opera-tion of the wireless power transmission system with a resonance-tuned transmission coil, the de-termination of the optimal energy parameters of wireless power transmission on the example of a magnetically coupled system with a series-sequential resonant circuit. The main results of the study are as follows: it is shown that the necessary condition for obtaining the maximum power in the receiver is not only the tuning to the resistance of the receiving coil, but also the inclusion of the receiving circuit to a certain active load resistance, depending on the coupling coefficient between the transmitting and receiving coils, and analytical expressions are obtained, by means of which it is possible to calculate the load resistance at which the considered power transmis-sion system has the maximum efficiency; it is proved that the transmission efficiency of the se-ries-sequential resonant circuit is maximum in the single resonance mode and depends on the coupling coefficient. Significance of the obtained results: It is proved that magnetic resonance transmission of electric energy in the case of series-sequential scheme of wireless power trans-mission is more effective in comparison with series-parallel, at frequencies not exceeding 100 kHz, which has a positive effect on the operation of charging stations for electric vehicles in an urban industrial environment and, as a result, on solving the problem of compliance of such sys-tems with electromagnetic compatibility requirements.

The purpose of this work is to substantiate the use of probabilistic components of the availabil-ity factor of the power supply system (PSS) of the enterprise consisting of a electric power sys-tem and autonomous loaded generators, to calculate overpayments for electricity. To achieve this purpose, operation of the PSS during the last 5 years was studied. The PSS was considered as a restored system which has spinning reserve. The process of the PSS functioning was repre-sented by a simple non-branching graph with one repair team and described by systems of dif-ferential and algebraic equations. The solution of the latter allowed calculating the probability components of the PSS availability factor. The operating time of the PSS in a calendar year was calculated with a different number of serviceable generators. The method for calculating over-payment for electricity was proposed. The most significant result is the justification of the use of probability components of the PSS availability factor for assessing overpayment for electricity. The significance of the obtained results lies in the fact that the proposed formula for overpay-ment for electricity allows to estimate overpayment, identify main causes and recommend measures to reduce it. The reliability of the results is confirmed by the example of calculating overpayment for electricity for a real enterprise. The newly obtained indicators of generator reli-ability can be used in the analysis of overpayment by other enterprises, which have similar gen-erators.

Іndustrial synthesis of gas hydrates is a promising area for storage, transportation and utilisation of natural gas. The aim of this work is to evaluate the factors that affect the efficiency of gas hydrate synthesis, in particular, the influence of gas microbubble generation. To achieve the goal, the following tasks were solved: the experimental unit was developed and assembled; the experimental research methodology was proposed; experimental data were obtained and their statistical analysis was performed; a mathematical model of hydrodynamic and heat and mass-exchange processes in the nozzle was developed; оn the basis of the mathematical model, a calculation program was created which allows analyzing the nozzle operation in different thermobaric conditions under different hydrodynamic regimes. The most important are the results of experiments, which allowed us to determine the factors that have the greatest influence on the intensification of propane hydrate synthesis: the presence of gas microbubbles, liquid temperature, gas temperature, gas pressure in the reactor and others. The influence of individual factors and the presence of gas mixture microbubbles on the intensity of mass transfer processes of hydrate formation in the reactor was determined. The influence of nozzle design features on the efficiency of hydrate formation was analyzed. The significance of the obtained results is to expand knowledge about the influence of various factors on the intensification of gas hydrate synthesis. The research results can be used for designing industrial plants for gas hydrate synthesis.

RELEVANCE: The relevance of this work lies in the study of new porous materials for use in compact, highly efficient heat exchange devices. PURPOSE: To investigate the hydro-aerodynamic properties of flows passing through porous inserts based on triply periodic minimal surface (TPMS) topologies. To develop a methodology for studying porous materials with ordered structures. To identify potentially suitable TPMS-based porous materials for application in heat exchange equipment. METHODS: Numerical (CFD) and experimental methods were used to address the research objectives. Ansys Fluent 2019 R3 software was utilized for numerical modeling. Experimental samples for the physical experiments conducted on the VENT-08-7LR-01 laboratory setup were fabricated using SLA additive technologies. The porosity of the samples ranged from 0.73 to 0.89. The experiment was conducted with inlet velocities ranging from 0.3 to 4.5 m/s. RESULTS: New empirical dependencies of pressure drop on flow velocity were obtained for inserts based on the surfaces: Primitive (P), Fischer Koch S (FKS), Neovius (N), and Schoen's I-WP (IWP). The airflow through the N structure showed the highest pressure drop, while the P structure had 8 times less pressure drop at the same velocity. Stagnation zones, which can negatively impact heat transfer, were identified in the porous inserts. Changes in local flow velocity in the porous inserts were determined to correlate with the insert's transparency. CONCLUSION: The research results can be used for designing cooling systems with TPMS-based ribbing. Based on the analysis of the velocity vector field distribution and pressure drops, the FKS and IWP structures have potential applications in heat exchange equipment.

RELEVANCE of the study lies in the development of an algorithm for controlling a DC voltage converter capable of providing a high-quality transient process with a wide change in the input parameters of the control object, affecting its nonlinear properties. purpose. THE PURPOSE. To consider methods for the development of continuous and discrete control systems for a step-up DC voltage converter and to obtain an analytical dependence of accounting for the nonlinear properties of the converter that affect the quality of regulation of the output voltage of the voltage stabilizer METHODS. The method of the average geometric root was chosen as a method for calculating the coefficients of the DC voltage converter control system, and the current circuit is adjusted using the technical optimum method, and the voltage circuit using the symmetric optimum method. The obtained analytical solutions of mathematical models and simulation models were compared in the SimInTech software environment. results. RESULTS. The simulation results show that the analytical solution of the mathematical model of the voltage stabilization system has a shorter transition time than the simulation results. This is due to the fact that in the mathematical model according to which the continuous control algorithm was synthesized, the presence of a power input filter, as well as the degree of their precharge, is not taken into account. Because of this, the transition time on simulation models is slower, applied 4 times than in the mathematical model. conclusion. CONCLUSION. When comparing the algorithms with each other, it can be seen that the algorithm obtained by converting a continuous algorithm by the Tusten method has the shortest transition time than all other algorithms. The continuous algorithm and the algorithm obtained by the inverse Euler transformation method have a speed close to each other, and the algorithm obtained by the direct Euler transformation turns out to be the slowest.