Electrical Research Articles

Power Factor Optimization with Cos Phi Mode Trainee

The power factor cos phi measures the efficiency of using electrical power in a system. A low power factor indicates the presence of high reactive power consumption, which can lead to increased energy losses and operational costs. To overcome this problem, an effective power factor regulation system is required. This research designed and developed the Cos Phi Trainer, which operates in two modes, manual and automatic, to improve the power factor at an electrical load. The system is designed to give users a practical understanding of measuring and improving power factors by setting up compensation capacitors. In manual mode, the user directly sets the value of the capacitor used to fix the power factor. While in automatic mode, the system uses current and voltage sensors to measure the power factor value in real-time and automatically adjusts the capacitor to achieve the optimal power factor. The trainer has an easy-to-use interface and a measurement panel that displays important parameters such as current, voltage, active power, reactive power, and power factor. The test results show that the system can significantly increase the power factor in manual and automatic modes. Thus, this Cos Phi Trainer is an effective tool for training in the laboratory of Indorama Engineering Polytechnic and teaching about electrical power management and energy loss reduction in the electrical system. Keywords: Power factor, manual, active power, reactive power, trainer

Evaporation characteristics of water droplets on heated surfaces with various coatings and under different wall thermal conditions

The anti-ice system is a crucial subsystem for ensuring aircraft safety. Water droplet evaporation on its surface is a frequent occurrence during flight. Investigating the characteristics of water droplet evaporation is essential for designing effective active or passive aircraft anti-ice systems. Previous research has primarily focused on the evaporation of small droplets under constant wall temperature conditions. The emergence of more electric aircraft has led to the adoption of electrical heating anti-ice systems, which typically operate under conditions of constant wall heat flux. Despite this shift, the quantitative characteristics of evaporation under different surface properties and constant wall heat flux conditions have not been thoroughly investigated. In this paper, an experimental test site was built to study the evaporation characteristics of water droplets on heating surfaces with various coatings and under different wall thermal conditions. The experimental results showed that the evaporation time for droplets on hydrophobic surfaces was longer than that on hydrophilic surfaces. The increase in evaporation time ranged from 5 to 13 times as the surface temperature was raised from 40 °C to 80 °C. Furthermore, the difference in evaporation time between small and large droplets was more pronounced under constant temperature conditions than that of constant heat flux conditions. For droplets on polished aluminum and hydrophilic surfaces, the evaporation rate was linearly related to the evaporation surface area. The findings of this study can inform future optimizations of anti-ice systems.

Design and simulation of a photovoltaic plant for maximum use of the solar resource in the Toluviejo municipality of Colombia

Currently, there are various technologies for constructing photovoltaic plants including monofacial and bifacial photovoltaic panels, centralized and string-type inverters, and fixed mounting structures with light followers, to build electrical energy generation systems. This work reviewed the advantages and disadvantages between these technologies, and the implementation of a photovoltaic plant in Toluviejo, Colombia was proposed. To justify and confirm the design, simulations were carried out with the PVsyst software, resulting in the bifacial panels, centralized inverters, and structures with light followers, the most effective solution, and with the highest production for the proposed photovoltaic power generation plant.

Research Progress on Current-Carrying Friction with High Stability and Excellent Tribological Behavior

Current-carrying friction affects electrical contact systems like switches, motors, and slip rings, which determines their performance and lifespan. Researchers have found that current-carrying friction is influenced by various factors, including material type, contact form, and operating environment. This article first reviews commonly used materials, such as graphite, copper, silver, gold, and their composites. Then different contact forms like reciprocating, rotational, sliding, rolling, vibration, and their composite contact form are also summarized. Finally, their environmental conditions are also analyzed, such as air, vacuum, and humidity, on frictional force and contact resistance. Additionally, through experimental testing and theoretical analysis, it is found that factors such as arcing, thermal effects, material properties, contact pressure, and lubrication significantly influence current-carrying friction. The key mechanisms of current-carrying friction are revealed under different current conditions, including no current, low current, and high current, thereby highlighting the roles of frictional force, material migration, and electroerosion. The findings suggest that material selection, surface treatment, and lubrication techniques are effective in enhancing current-carrying friction performance. Future research should focus on developing new materials, intelligent lubrication systems, stronger adaptability in extreme environments, and low friction at the microscale. Moreover, exploring stability and durability in extreme environments and further refining theoretical models are essential to providing a scientific basis for designing efficient and long-lasting current-carrying friction systems.

A Novel Bladeless-Spinal Wind Turbine Design for Efficient Energy Generation in Low wind Speeds

Wind energy is a widely used and renewable source for electricity generation. However, traditional wind turbines face challenges such as large footprints, noise, high costs, and efficiency limitations. To address these issues, bladeless wind turbines (BWTs) have gained popularity, and extensive research is being carried out to develop efficient designs. In this work, we propose a novel BWT design, inspired by the spine structure found in human bodies. The BWT architecture is modular, autonomous, and robust, making it suitable for small, portable, and small-scale applications. Our BWT design was inspired by the spine structure found in the human body and other vertebrates. The BSWT architecture is modular, autonomous, and robust. Detailed 3D models were created using Fusion 360 and SolidWorks software to refine and iterate the design. Our BWT features a complete electrical system with an energy harvesting base, a rectifier, converter, and a storage battery. The stored energy is then converted to AC and connected to the load. Our design utilizes oscillating rods divided into sections, enabling energy generation even at low wind speeds. The casing protects the rods from shear stresses and corrosion. While practical applicates involve a spine design with multiple sections, our experiments primarily focus on a single turbine.

The Biomimetic Electrical Stimulation System Inducing Osteogenic Differentiations of BMSCs.

Electrical stimulation has been used clinically as an adjunct therapy to accelerate the healing of bone defects, and its mechanism requires further investigations. The complexity of the physiological microenvironment makes it challenging to study the effect of electrical signal on cells alone. Therefore, an artificial system mimicking cell microenvironment in vitro was developed to address this issue. In this work, a novel electrical stimulation system was constructed based on polypyrrole nanowires (ppyNWs) with a high aspect ratio. Synthesized ppyNWs formed a conductive network in the composited hydrogel which contained modified gelatin with methacrylate, providing a conductive cell culture matrix for bone marrow mesenchymal stem cells. The dual-network conductive hydrogel had improved mechanical, electrical, and hydrophilic properties. It was able to imitate the three-dimensional structure of the cell microenvironment and allowed adjustable electrical stimulations in the following system. This hydrogel was integrated with cell culture plates, platinum electrodes, copper wires, and external power sources to construct the artificial electrical stimulation system. The optimum voltage of the electrical stimulation system was determined to be 2 V, which exhibited remarkable biocompatibility. Moreover, this system had significant promotion in cell spreading, osteogenic makers, and bone-related gene expression of stem cells. RNA-seq analysis revealed that osteogenesis was correlated to Notch, BMP/Smad, and calcium signal pathways. It was proven that this biomimetic system could regulate the osteogenesis procedure, and it provided further information about how the electrical signal regulates osteogenic differentiations.

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance.

Research on Large Hybrid Electric Aircraft Based on Battery and Turbine-Electric

Hybrid electric aircraft use traditional engine and electric propulsion combinations to optimize aircraft architecture, improve propulsion efficiency, and reduce fuel consumption. As a new technology, the fuel and energy consumption calculation of hybrid electric aircraft is more complicated than traditional aircraft due to the usage of different energy forms. The purpose of this paper is to develop the analytical method for fuel and energy consumption for hybrid electric aircraft. This paper summarizes the working principle of hybrid electric aircraft, including the system architecture and power conversion mechanism. The calculation of fuel and energy consumption for hybrid electric aircraft is carried out in detail. In order to evaluate large hybrid electric aircraft, the architecture, based on energy flow, is established, and turbofan engine, electrical system, electric duct fan, and aerodynamic model characteristics are established. With a single-aisle aircraft as an example, the fuel and energy consumption under the 800 nautical mile range is performed. It shows that fuel consumption can be reduced by 10% and energy consumption by 4.7% compared with a traditional aircraft. The effects of different range and battery ratios are analyzed. The payload range for the hybrid electric aircraft is analyzed. The results show that even though the hybrid electric aircraft reduces the payload and range, it can significantly reduce fuel and energy consumption.

Optimal battery electric bus system planning considering heterogeneous vehicles, opportunity charging, and battery degradation

Battery electric buses (BEBs) have been seen as a viable alternative for sustainable mobility. The charger placement and fleet configuration play a crucial role in the operational efficiency of BEBs. In this study, a joint optimization model is developed to strategically plan the charger placement and fleet configuration for a BEB system. Distinct from existing research, we simultaneously incorporate opportunity charging, heterogeneous vehicles, and battery degradation to integratively minimize the costs of charger installation, BEB purchase, energy consumption, and battery degradation. Based on a model reformulation, a heuristic-exact hybrid solution method is customized to solve the model. A real-world case study from Oslo, Norway is provided to demonstrate the optimization model and solution method. The results indicate that the proposed model can reduce the total costs by 21.27 % in comparison to the conventional model. Sensitivity analyses are further conducted to provide insightful findings and managerial implications for BEB systems.

Sinusoidal Atrial Fibrillation (Yasser’s Fibrillation) and Partial SAN Function in COVID-19 Pneumonia; a New Cardiovascular Discovery Change in Atrial Fibrillation Directory

Introduction: Atrial fibrillation (AF) is one of the most common and probable serious arrhythmias and a hallmark of an increasing risk of pathological thrombus formation. Interestingly, normal sinus rhythm (NSR) is the standard rhythm in a normal person. The sino-atrial node (SAN) is the heart's pacemaker and generator of the normal electrical conduction system of the heart which allows for the generating of electrical impulses. In AF, the normal regular electrical impulses generated by the sinoatrial node are overwhelmed by disorganized electrical waves, usually originating from the roots of the pulmonary veins. These disorganized waves conduct intermittently through the atrioventricular node (AVN) leading to irregular activation of the ventricles that generate the heartbeat. Cases presentation: Four different cases were described. 1. An elderly female patient presented to the ICU with metastatic complicated Mantle cell lymphoma, COVID-19 pneumonia, and irregular heart rate. 2. A senile male patient presented to the intensive care unit (ICU) with an aortic aneurysm, COVID-19 pneumonia, consumptive thrombocytopenia, lung fibrosis, and irregular heart rate. 3. A middle-aged female presented to the ICU with chronic renal failure (CRF) on regular hemodialysis, liver cell failure (LCF), pseudo-orange abdominal cellulitis, valvular heart replacement, and irregular heart rate. 4. An elderly female patient presented with cardiomyopathy, global cardiomegaly, irregular heart rate, diabetes, pseudo-orange abdominal cellulitis, liver cirrhosis, and COVID-19 pneumonia. Method of study and patients: Retrospective-Observational four case report series study was conducted in Kafr El-Bateekh Central Hospital staring from January 21, 2023 and ending on June 24, 2024 on COVID-19 pneumonia and atrial fibrillations. Conclusion: Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF are a new cardiovascular discovery. The partial sino-atrial nodal function has essential role in the presence of Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF and its interpretation. Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF may be balanced between AF and normal sinus rhythm. The percentages of normal sinus beats to AF beats in the cases of Sinusoidal atrial fibrillations (Yasser’s fibrillation) may be a guide for approximate healthy or sick part of the sinoatrial node. Widening the research for the Sinusoidal atrial fibrillations (Yasser’s fibrillation) or mixed AF will be recommended.

Fuzzy Logic-Based Energy Management Strategy for Hybrid Fuel Cell Electric Ship Power and Propulsion System

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems.

GSM Based of Unclad Fibre Temperature Sensor for Electrical Transmission Cable Monitoring System

This paper presents a temperature monitoring system for an electrical transmission cable utilizing a global mobile service (GSM)-based unclad fibre sensor. The developed system consists of GSM module, Arduino controller, unclad plastic optical fibre (POF) sensor, light emitting diode (LED) and photodetector. The unclad POF sensor was chosen due to its immunity towards electromagnetic interference as well as its high sensitivity performance. In this work, the transmission cable hazardous temperature is set at 70oC, which is the maximum operating temperature value provided by the National Electrical Code (NEC) NFPA 70. For system characterization, temperature of the cable was varied from 30oC to 70oC. Photodetector output voltage for this temperature variation were recorded at 660 nm and 770 nm operating wavelength. The recorded data shows that the sensor sensitivity performances at 660 nm wavelength are found superior than 770 nm wavelength, which are 0.9710mV/oC and 0.9699mV/oC respectively. As for the GSM communication system, the system was characterized based on accuracy of the information send via GSM text message and its delay. The developed system offers economical and practical monitoring system to be adopted for transmission cable temperature monitoring in remote area.

A novel variable neighborhood descent algorithm for service restoration in radial electrical distribution networks

Abstract This paper presents a variable neighborhood descent algorithm for the service restoration problem (SRP) in electrical distribution systems. The restoration problem appears when a permanent fault occurs in the system. The fault must be localized and isolated from the rest of the system. As a consequence of the isolation, the downstream area is de-energized and requires restoration. The variable neighborhood descent algorithm features its ability to solve the SRP for radial distribution systems and introduces a strategy that helps to deal with the system’s radiality. Tests are performed using a 53-node test system. The results show that the proposed algorithm can efficiently solve the SRP in a distribution system, and as a deterministic algorithm, the results can be used to help nondeterministic algorithms.

Ground-breaking approach to enabling fully solar renewable energy communities

Renewable Energy Communities are an important strategy of the EU, to promote and optimize the distributed deployment of renewable generation systems. At current incentives and costs, RECs that share the energy produced by PV/battery systems are expected to take the lion's share of distributed generation. In this paper, we highlight some important critical issues in electric system management that could result from the deployment of RECs if PV/battery system generation cannot meet the entire community demand. Thus, we show how a flexible PV system (PV/battery systems able to provide firm/dispatchable generation) can be cost-effectively dimensioned to supply 24/365 96%–97 % of the demand of a residential users' community. We demonstrate that flexible PV systems of 1/10 MWp with 1.2/12 MWh of storage capacity can supply communities of 142/1420 residential users at a least production cost of 0.12/0.096 €/kWh. We further provide two business models based on power purchase agreement to demonstrate that, at these production costs, fully solar RECs are currently techno-economically feasible and provide mutual benefits to both flexible PV producers and REC members. Suitable virtual corporate PPAs can reduce the electric bill of the REC members by 11%–5% without requiring any investments and increase the producer incomes by 18%–22 %.

A Novel Electric Vehicle Thermal Management System Based on Charging Station Heat Pump System During Fast Charging

Abstract A battery thermal management system based on a charging station heat pump system is proposed to improve battery charging efficiency during high-power DC charging. The system provides coolant of appropriate temperature through the charging station heat pump system. It enables the battery to be charged at the optimal temperature for charging, which improves the charging efficiency and reduces the charging time. The two system models are modeled and analyzed using numerical simulation software, and the temperature characteristics and charging time of the proposed system and the original battery thermal management system based on the electric vehicle heat pump system are analyzed under five different temperature conditions. The results show that the proposed system has fast response, high efficiency, and can maintain the maximum temperature below 50°C. Compared with the original system, the proposed system reduces the charging time of the power battery by 10.6%, 17.9%, and 24.6% at 0°C, −10°C, and −20°C operating conditions, respectively. It also saves 1.4% and 6.0% of the charging time at 20°C and 40°C operating conditions, respectively. Comparing the charging time for the power battery at each stage, the proposed system mainly reduces the charging time in the range of 0-20% of the battery state of charge compared to the original system.

Modeling and Simulation of an Integrated Synchronous Generator Connected to an Infinite Bus through a Transmission Line in Bond Graph

Most electrical energy generation systems are based on synchronous generators; as a result, their analysis always provides interesting findings, especially if an approach different to those traditionally studied is used. Therefore, an approach involving the modeling and simulation of a synchronous generator connected to an infinite bus through a transmission line in a bond graph is proposed. The behavior of the synchronous generator is analyzed in four case studies of the transmission line: (1) a symmetrical transmission line, where the resistance and inductance of the three phases (a,b,c) are equal, which determine resistances and inductances in coordinates (d,q,0) as individual decoupled elements; (2) a symmetrical transmission line for the resistances and for non-symmetrical inductances in coordinates (a,b,c) that result in resistances that are individual decoupled elements and in a field of inductances in coordinates (d,q,0); (3) a non-symmetrical transmission line for resistances and for symmetrical inductances in coordinates (a,b,c) that produce a field of resistances and inductances as individual elements decoupled in coordinates (d,q,0); and (4) a non-symmetrical transmission line for resistances and inductances in coordinates (a,b,c) that determine resistances and inductance fields in coordinates (d,q,0). A junction structure based on a bond graph model that allows for obtaining the mathematical model of this electrical system is proposed. Due to the characteristics of a bond graph, model reduction can be carried out directly and easily. Therefore, reduced bond graph models for the four transmission line case studies are proposed, where the transmission line is seen as if it were inside the synchronous generator. In order to demonstrate that the models obtained are correct, simulation results using the 20-Sim software are shown. The simulation results determine that for a symmetrical transmission line, currents in the generator in the d and q axes are −25.87 A and 0.1168 A, while in the case of a non-symmetrical transmission line, these currents are −26.14 A and 0.0211 A, showing that for these current magnitudes, the generator is little affected due to the parameters of the generator and the line. However, for a high degree of non-symmetry of the resistances in phases a, b and c, it causes the generator to reach an unstable condition, which is shown in the last simulation of the paper.

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach

An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.

Optimizing integrated energy systems at park level: A cooperative game approach with exergy economics

This study presents a comprehensive framework for optimizing energy systems by integrating exergy analysis, energy economics, and game theory. The concept of exergy, which quantifies the usable energy within a system, is employed to evaluate energy efficiency and losses across various energy sources, including thermal, cooling, chemical, and electrical systems. Stoichiometric coefficients, denoted by the factor λ, are utilized to simplify exergy calculations for different energy types and processes. The economic evaluation of energy flows is conducted through energy economics principles, incorporating cost allocation and balance equations. The integration of game theory into the optimization model ensures strategic interactions among energy components, leading to a Nash equilibrium that balances economic performance, efficiency, and environmental sustainability. The model also accounts for emissions and the required proportion of renewable energy. To solve the complex optimization problem, a modified Particle Swarm Optimization (PSO) algorithm is employed, featuring adaptive mechanisms for velocity and inertia updates, enhancing the search process for the optimal solution. The proposed framework is designed to optimize the Integrated Energy System (IES) efficiently, ensuring sustainable and economically viable energy management. The analysis of optimization strategies highlights a trade-off between cost and efficiency. Strategy 1, focused on minimizing cost, achieves the lowest cost at 4069.37 CNY, 25.79 % less than Strategy 2, but with a reduced exergy efficiency of 59.82 %, which is 10.14 % lower than Strategy 2′s 68.47 %. Strategy 3 offers a balanced approach, with a cost of 4970.89 CNY, 9.43 % higher than Strategy 1 but 9.43 % lower than Strategy 2. It achieves an exergy efficiency of 67.87 %, only 0.60 % lower than Strategy 2, thus providing a practical compromise between economic performance and efficiency.

A joint optimization strategy for electric vehicles and air conditioning systems with building battery configuration

Building air conditioning systems, electric vehicles and battery energy storage systems all provide substantial flexibility for grid operations. However, the joint optimization strategy involving these three demand response resources in buildings has been infrequently studied. This research proposes a day-ahead optimization strategy to coordinate the joint operation of air conditioning systems and electric vehicles. In this framework, cooling load is predicted using an autoregressive exogenous model, while electric vehicle charging load is predicted through Monte Carlo simulations. An evaluation method is introduced to assess the comprehensive benefits of the demand response strategy, considering thermal comfort, economic efficiency, and grid friendliness. Using an office building in Tianjin, China, as a case study, the results indicate that the joint optimization strategy reduces operational costs by 3.71 % and peak electricity load by 38.62 % compared to the original strategy. Furthermore, it enhances the grid friendliness of the energy system by 42.64 %. The configuration of the battery energy storage system is also explored in conjunction with the optimization strategy to further improve grid friendliness. The impact of economic factors and thermal comfort on the configuration of the battery energy storage system is discussed. In the case study, raising the upper temperature limit by 1 °C can save at least 17.1 % in capacity, while battery energy storage system investment and operational costs can respectively be reduced by 55.04 % and 27.14 % within the thermal comfort range.

Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system.