Model Of Electric Power System Research Articles

Modeling of double ground fault modes in power supply systems of non-traction consumers of railway transport

THE PURPOSE. Development of digital models to determine the modes of double ground faults in 6-10 kV overhead signaling lines supplying signaling, centralization and automatic blocking facilities.METHODS. To create the models, methods of multiphase modeling of electric power systems were used, based on the use of lattice equivalent circuits from RLCelements connected according to complete graph circuits. For modeling, the industrial software package Fazonord, developed at the Irkutsk State Transport University, was used.RESULTS. The developed models provide correct accounting of all factors influencing the modes of double ground faults, including increased electromagnetic influences of the traction network. In contrast to known approaches, the dynamics of changes in these influences caused by variations in traction loads during train movement are modeled.CONCLUSION. The proposed approach to modeling double ground fault modes is universal and can be used for standard traction power supply systems, as well as promising high-voltage traction networks. The results obtained can be used in practice to configure relay protection devices for overhead signaling lines in order to reliably detect double ground fault modes and take measures to eliminate them. In addition, the modeling methodology presented in the article can be used when selecting measures to improve the reliability of power supply to signaling and automatic blocking facilities that ensure the safety of train traffic, as well as to develop methods and algorithms for determining distances to places of double ground faults in overhead power lines feeding signaling facilities , centralization and autoblocking. The presented results confirmed the effectiveness of the proposed approach for modeling double ground fault modes.

An improved energy management strategy for hybrid electric powered aircraft based on deep reinforcement learning

Electric airplanes have attracted a great deal of attentions because of their strategic commercial values and low emissions. One of the essentials for efficient control and management of hybrid electric propulsion aircraft is a well-designed energy management strategy (EMS). A new EMS approach based on deep learning is introduced in this study to realize thrust distribution and power distribution, consequently lowering aircraft fuel consumption. A model of hybrid electric propulsion power system including turbine engines and lithium-ion battery pack is constructed to act as an application environment. An intelligent energy management strategy based on deep reinforcement learning (DRL) is then proposed in this paper. Two DRL agents based on twin delayed deep deterministic policy gradient (TD3) are combined in the hierarchical framework. The multi-agents are trained to handle the energy management problem in this paper. In a finite period of time, the training process is accomplished. After training, the simulation result of the test shows that this DRL-based EMS reduces the equivalent fuel consumption by 2.2% compared with that using the existing method, and preserves 99.9% of the battery state of health (SOH). The battery pack's state of charge (SOC) regresses precisely to the reference value at the end. Particularly, the proposed EMS is able to work properly for different flight profiles and various initial SOCs. The results of this paper provide preliminary supports for the feasibility and superiority of deep reinforcement learning applied to the energy management strategy of series hybrid electric aircraft.

Balancing the Water-Carbon Trade-Off: Development of a Bi-Level Source-Grid-Load Synergistic Optimization Model for Multi-Regional Electric Power System

This study introduces the Bi-Level Source-Grid-Load Synergistic Optimization (BL_SGLSO) model, which effectively balances the competing objectives of water conservation and carbon emission reduction in the power industry. The model aims to establish a clean and low-carbon electric power system by harmoniously reconciling these two critical goals. Through the application of bi-level programming, the BL_SGLSO model adeptly manages the preferences and conflicts of decision makers at various levels while capturing regional interactions and the intricacies of electricity transmission. Key findings reveal that non-fossil energy conversion technologies are poised to become the dominant force in electricity generation, accounting for an impressive 89.34% share by 2050. To mitigate the spatial mismatch between power load and resource allocation, the development of new transmission pathways and the expansion of the “power transmission from west to east” initiative are paramount. Furthermore, the implementation of a carbon-reducing power system offers significant potential for conserving water resources and alleviating water stress. These insights provide invaluable guidance for decision makers seeking to optimize multi-regional electric power systems for both water efficiency and low-carbon outcomes while simultaneously promoting the adoption of renewable energy sources and fostering synergistic development across regions.

Quenching chaos in a power system using fixed-time fractional-order sliding mode controller

The aim of this paper is to study the unwanted chaotic oscillation that can severely affect the reliable and safe operation of electrical power systems. The dynamical behavior of a benchmark three-bus nonlinear electrical power system model is explored using modern nonlinear analysis methods, where the Lyapunov exponents spectrum, bifurcation diagram, power spectral density and bicoherence are used to investigate the chaotic oscillation in the power system. The analysis shows the existence of critical parameter values that may drive the power system to an unstable region and can expose the system to bus voltage collapse and angle divergence or blackout. To eliminate the chaotic oscillation, a fractional-order fixed time sliding mode controller has been used to control the power system in a finite time that can be predetermined by the designer. The Lyapunov theorem has been used to prove the stability of the controlled power system. The results confirm the superiority, robustness, and effectiveness of the suggested control algorithm.

Application of Phase-Shifting Transformer with Longitudinal and Transverse Voltage Regulation

This article was written to present a new solution for a phase-shifting transformer and to show that, in many aspects, the proposed device is more functional and improved when compared to the other currently available phase-shifting structures. The article shows the construction of the new phase-shifting transformer, with longitudinal and transverse voltage regulation. The proposed device’s functionality and available operation states are compared to other existing solutions. Selected steady-state variants of the operation of the proposed transformer were checked based on computer simulations and the laboratory model presented in detail in the article. Convergence between measurements and simulations was achieved. Power flow calculations were also performed in the five-node electrical power system model. In these calculations, two variants of the proposed device’s work were considered and compared with the asymmetric phase shifter. The tests confirmed in the proposed structure, both angle and voltage regulation, enabled maintaining a constant active power flow. The authors show that the proposed solution combines the advantages and functionality of various types of phase shifters while, at the same time, being characterized by a much simpler structure, which results in a two-times-lower cost of the device.

Novel method for setting up the relay protection of power systems containing renewable energy sources and hydrogen energy storage systems

Integration of renewable energy sources (RES) together with energy storage systems (ESS) changes processes in electric power systems (EPS) significantly. Specifically, rate of change and the lowest values of operating conditions during the emergencies are got influenced. Such changes can cause incorrect actions of relay protection (RP) as it was designed and adjusted with no regard for influence of RES and ESS. Detailed research on processes during the different normal and abnormal modes in both EPS and primary transducers and also in RP devices should be done to take preventive actions. To do this research mathematical modeling based on detailed and authentic models of all elements including RP should be used. HRTSim (which was developed by authors) software for simulating EPS provides the opportunity to create such models of EPS of any size without simplifications and limits. Using of this instrument together with detailed mathematical models of RP which were developed before provided the opportunity to investigate them rigorously in RES-integrated EPS. Settings providing adequate action of RP in certain conditions were performed as a result of this investigation. Fragments of these investigations are performed in this paper. Results of these investigations would be useful for designing new methods and tools of RP adjustment.

A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles

Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the ”dimension disaster” once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.

Fault Alarms and Power Performance in Hybrid Electric Vehicles Based on Hydraulic Technology

In order to improve the fault alarm effect on the power performance of hydraulic hybrid electric vehicles (HEV), this paper proposes a fault alarm method for hybrid electric vehicle power performance based on hydraulic technology, builds a hybrid electric vehicle power system model, uses hydraulic technology to extract the characteristic signals of key components, uses support vector mechanisms to build a hybrid electric vehicle classifier, and obtains the fault alarm results for dynamic performance based on hydraulic technology. The results show that the proposed method can improve real-time diagnosis and alarm for engine faults in HEV, and the fault can be diagnosed after 5 s of injection, thus ensuring the dynamic stability of HEV.

Methods for the Formation of Calculation Models of Electric Power Systems in the Adequacy Assessment Problem

The article deals with the problem of generating calculation models (CM) of electric power systems (EPS) into reliability zones, designed to perform calculations of adequacy assessment indicators. The substantiation of the depth of detail of the CM is based on taking into account two properties of the EPS: reliability, namely, on the basis of calculated estimates of the limits of applicability of deterministic and probabilistic reliability and efficiency criteria, namely, on the basis of a comparison of cost estimates of measures to ensure reliability. To solve this problem, it is proposed to use a number of so-called engineering algorithms, which take into account the reliability and efficiency of the EPS. The experimental part presents the results of applying the proposed algorithms for the formation of the CM analyzed by the EPS.

Концептуальное представление энергетического перехода в электроэнергетике региона в новых реалиях

The energy transition is a global problem affecting environmental and energy security, sustainable economic development, social well-being. However, since its implementation differs in certain regions, it is necessary to develop specific approaches that consider local factors, external threats and risks. The article proposes a conceptual vision of energy transition at the regional level in the context of crisis, economic and logistical changes, and socio-economic, scientific and technical transformations. It is hypothesised that in order to implement the energy transition, a compensation method is required to ensure the equivalence of environmental, economic and technical results. The study analysed expert opinions published in foreign and Russian journals indexed in the SCOPUS, Science Direct, MDPI, WIT Press databases, as well as analytical reports of international energy agencies and consulting firms. The analysis revealed that the most appropriate form of organisation of the regional electric power system is a flexible model combining system and regional circuits and involving power plants with a wide range of generating capacities. The integrated power system and decentralised energy sources are seen as energy transition objects. The study presented an energy transition scheme from the implementation of low-cost energy demand management measures to the reconstruction of coal-fired power plants and construction of renewable energy facilities. An ideal model of the regional electric power system was developed as a strategic guideline in the energy transition implementation. Possible scenarios and stages of the energy transition were discussed. The findings can be applied by government and industry regulators, energy companies, large electricity consumers when planning structural, organisational and market transformations in the regional energy sector under uncertainty.

Dynamic Phasor Finite-Element Modeling of a DFIG for Grid Connection Studies

Co-simulation studies of electric power systems and electric machines have been always a challenge. In order to reduce the simulation time to a reasonable value, lumped-parameter Electric-Machines models are commonly used in electric power system modeling software packages to avoid the heavy computational burden of more accurate modeling methods especially Finite Element Method (FEM) on the expense of less accuracy. The proposed technique in this paper combines the Dynamic Phasor Modeling technique for power system simulations with FEM to accurately model the DFIG while connected to the grid. The utilization of dynamic phasors enables adopting large simulation time-steps resulting in a significant reduction in the simulation time compared to the conventional time-domain FEM modeling. The mathematical foundation of the proposed modeling method is presented including the generator's core saturation. Custom-written C++ codes have been developed by the authors to execute the new dynamic phasor FEM algorithm and the conventional time-domain FEM in order to fairly compare their accuracy and numerical performances. As the proposed method combines time and frequency domains, a unique capability of modeling the rotor movement can be achieved. The rotation can be represented by physically incrementing the rotor and airgap mesh as in regular time-domain solvers, by mathematically representing the rotation using the Virtual Blocked Rotor method as in frequency-domain solvers, and the proposed method of combining the two aforementioned approaches. The three methods of modeling rotor rotation are discussed and their simulation results are compared to give a guide to choose the proper method for the different modeling targets. The results show that the proposed dynamic phasor FEM is capable of producing comparable results to the traditional time-domain solver at a substantially reduced simulation time.

Investigating the influence of lithium-ion batteries degradation on the parameters of the electric power storage system

This article deals with the use of a battery-based energy storage system (ESS) to ensure the required power output of power plants (PP) based on renewable energy sources (RES) integrated into the electric power system (EPS). A model of a lithium-iron-phosphate battery-based ESS has been developed that takes into account the calendar and cyclic degradation of the batteries, and the limitations of the conversion subsystem. The nominal capacity and power of the ESS is proposed to be chosen based on two levels of tolerances: the preset range of RES-based power output and the relative period of deviation from the committed power. When choosing the ESS parameters, the features of its operation, as well as restrictions on the part of the EPS, were taken into account. The developed method was applied to the EPS model including solar power plants (SPP) and wind power plants (WPP). In the end of the article, the obtained results are analyzed and the effect of the ESS operation on its residual capacity and service life is shown.

Оценка влияния электрических станций с возобновляемыми источниками энергии на работу релейной защиты и автоматики в электроэнергетической системе

The article deals with the issue of the influence of solar power plants on the operation of relay protection and automation devices. The development of simulation models of electric power systems in the Simulink MATLAB environment has been carried out for three cases: power is supplied entirely from a solar power plant; power is supplied from the traditional source of the electric power system; the power is supplied partly from the solar power plant, partly from the traditional source of the electric power system. The description of the main parameters of the blocks of the simulation model is made. A study was also made of the influence of renewable energy sources on the sensitivity of relay protection, which directly depends on the level of short-circuit currents. Graphs of the dependence of the two-phase short circuit current on time are plotted. The conclusion is made about the decrease in the sensitivity of the relay protection in the presence of a solar power plant in the electric power system. The results obtained make it possible to calculate the parameters of various operating modes of the system, the results of which can be used to configure relay protection and automation devices.

Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.

REACTIVE POWER MARKET MODEL BASED ON THE DIVISION OF THE ELECTRIC POWER SYSTEM INTO VOLTAGE ZONES AND ON THE TWOLEVEL OPTIMIZATION ALGORITHM

The article presents a reactive power evaluation methodology which in terms of time comprises the action of the reactive power market in the stage of short-term planning. The methodology is based on the division the electric power system (EPS) into voltage zones by using the electrical distance method, whereby local reactive power markets are established. Within each local market a separate TSO cost optimization is performed by means of the optimal power flow applying the criterion of minimal required payment for reactive power. The said optimization is the first step within a developed methodology, the purpose of which is to determine the share of each of the available control systems in reactive power generation within each of the established zones. Based on optimal reactive power generation, an auction procedure is conducted by which a uniform price of reactive power generation within a zone is defined. In the second optimization step the optimization of the whole network is carried out by the criterion of minimal energy procurement costs to cover the network losses and simultaneously by the criterion of minimal reactive energy procurement costs, using the optimal power flow. The methodology has been tested on the Croatian EPS model for the maximum network load scenario. The proposed approach is warranted by the results, whereas the detected problems can at once serve as guidelines for future development and potential improvements in the proposed methodology.

Simulation-Based Analysis of Dynamics of Autonomous Electric Power Systems

This paper is devoted to the development of a model of an autonomous electric power system to study random processes of voltage, current and power changes in emergency and dynamic operation modes. A diagram for calculating short-circuit currents has been presented, which was focused on a typical wide range of autonomous power plants with three diesel generator units. A model of an autonomous power plant has been developed, allowing to solve the assignments of determining short-circuit currents and starting currents of electric power machines. The equivalent network of the studied power system for transient calculations has been presented. Thus, the voltage waveforms have been obtained. A comparative assessment of theoretical calculation methods and simulation analysis demonstrated a high degree of accuracy of the simulation results. The use of approach suggested in the article and the developed model allows to increase the accuracy of conclusions when testing the abruptly variable processes and to make the most reasonable choice of measures to improve the quality of electricity and the reliability of electrical equipment. In particular, simulation analysis and obtaining transient curves for starting powerful consumers allow more accurately choose the type of circuit breakers used and the related configuration parameters.

Energy Management Strategy in 12-Volt Electrical System Based on Deep Reinforcement Learning

The increasing electrification in motor vehicles in recent decades can be attributed to higher comfort and safety demands. Strong steering and braking maneuvers reduce the vehicle’s electrical system voltage, which causes the vehicle electrical system voltage to drop below a critical voltage level. A sophisticated electrical energy management system (EEMS) is needed to coordinate the power flows within a 12-volt electrical system. To prevent the voltage supply from being insufficient for safety-critical consumers in such a case, the power consumption of several comfort consumers can be reduced or switched off completely. Rule-based (RB) energy management strategies are often used for this purpose, as they are easy to implement. However, this approach is subject to the limitation that it is vehicle-model-specific. For this reason, deep reinforcement learning (DRL) is used in the present work, which can intervene in a 12-volt electrical system, regardless of the type of vehicle, to ensure safety functions. A simulation-based study with a comprehensive model of a vehicle electric power system is conducted to show that the DRL-based strategy satisfies the main requirements of an actual vehicle. This method is tested in a simulation environment during driving scenarios that are critical for the system’s voltage stability. Finally, this is compared with the rule-based energy management system using actual vehicle measurements. Concluding measurements reveal that this method is able to increase the voltage at the most critical position of the 12-volt electrical system by approximately 0.6 V.

A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.

Modeling and simulation analysis of electric forklift energy prediction management

In recent years, the effective utilization of energy for electric forklift has been the focus of research. However, the load of forklift change markedly and frequently, leading to a great challenge in the performance of the composite energy system. To solve this problem, this paper presents the power system structure of electric forklift and the battery–supercapacitor hybrid energy management method of electric forklift truck. The working conditions and energy flow of electric forklift are analyzed and the power demand forecasting model of electric forklift power system is indispensable. Two predictive control based on Markov chain and neural network are developed, considering the performance of predictive control. The predictive model is applied to the stochastic model predictive (SMPC) energy management strategy. Simulations are carried out on the representative driving cycles of an electric forklift. As an auxiliary power supply on the energy management of composite power, the influence of the development depth of supercapacitor is discussed. Simulation results show that short-term Markov chain model is closer to the actual working condition. Fuzzy neural network model can give full play to the charging and discharging characteristics of supercapacitors. The state of charge of supercapacitor is stable between 0.5 and 1, which effectively stabilizes the rate of the charge and discharge of the battery.

Fault Diagnosis Technology for Ship Electrical Power System

This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power system, the simulation model of the ship electrical power system is built using the MATLAB/Simulink simulation software platform in order to understand the normal working state and fault state of the generator and load in the power system. Then, the model is simulated to generate the fault response curve, and the picture dataset of the network model is obtained. Second, a CNN fault diagnosis model is designed using TensorFlow, an open-source tool for deep learning. Finally, network model training is performed, and the optimal diagnosis results of the ship electrical power system are obtained to realize structural parameter optimization and diagnosis. The diagnosis results show that the established simulation model and improved CNN can provide support for fault diagnosis of the ship electrical power system, improve the operation stability and safety of the ship electrical power system, and ensure safety of the crew.