Emergency Power System Research Articles

Experimental implementation of extremum-seeking control: Gas fuel efficiency at electrical generation under power requirements

A current challenge stands for operating the emergency power system (EPS) that involves the challenge of supplying sufficient electrical energy at same time the fuel consumption is minimum. A complication arises as power requirements change during the EPS operation which can be seen as uncertain load disturbances. In such a context, the extremum-seeking (ES) is alternative towards the efficient energy conversion when control goal involves fuel optimization along the time operation. Here, the dynamical response of a gas fueled power plant is identified via Hammerstein model. The model is realized such that an ES control is designed for automatically reaching an extreme in face to distinct (unmeasured and uncertain) power requirements. The ES control is designed and experimentally tested at an electrical generator at distinct power requirements. The results show the minimum gas fuel consumption remains in face to distinct power requirements.

Intelligent Power System Stability Assessment and Dominant Instability Mode Identification Using Integrated Active Deep Learning.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.

WITHDRAWN: Application of Data Mining Technology in Analysis of Influencing Factors of Smart Electric Power Emergency Rescue

Because electric emergency rescue is affected by many factors, there are certain problems in the rescue process. In order to improve the power emergency rescue effect, based on data mining technology, this paper selects the neural network algorithm as the main analysis algorithm of the model, and uses the multi-layer forward neural network topology to perform modeling and correlation analysis. Moreover, this paper establishes the functional architecture of the power system emergency plan deduction system based on the analysis of the demand for the power system emergency plan deduction system. In addition, this paper combines the power emergency rescue system requirements to build the model function module, proposes a mobile model based on the emergency rescue scene, and analyzes its realization process. Finally, in order to study the system performance, this paper designs experiments to analyze the model performance, and combines mathematical statistics to draw charts and score the system model. The research results show that the power rescue influence factor analysis model constructed in this paper is effective.

A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions

Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.

Innovative Controller and Remote Battery Capacity Measurement System

The article aims to present a system capable of managing emergency power systems in an automated, digital, precise, economical and environmentally friendly manner, ensuring continuity of energy supply. Modern technological progress has resulted in an increased demand for advanced security systems capable of processing data in real-time. As devices become more and more integrated with electronic data collection and processing systems, the importance of energy autonomy in securing these systems in emergencies increases. This challenge is particularly important for entities managing extensive and geographically dispersed technical infrastructure, such as telecommunications, services railways, pipeline maintenance and military operations. Ensuring energy autonomy for such enterprises requires periodic technical assessment of energy storage solutions, especially batteries.

An efficient integrated model for diesel‐engine three stage brushless synchronous generator

AbstractBrushless power generation sets are a technology of choice for on‐board power generation in aerospace and marine applications, emergency and backup power systems, etc. Efficient modelling of these systems is necessary for various purposes such as design, analysis, control and specially condition monitoring. An accurate and efficient model of a diesel‐engine generator‐rectifier system consisting of a three stage brushless synchronous generator is presented. The constant parameter voltage‐behind‐reactance model of the pilot exciter (PE), main exciter (ME) and main generator are derived and integrated to form a unified model suitable for efficient simulation of a diesel‐engine brushless generation system. Simulation studies are carried out in MATLAB using PLECS toolbox and the results are compared to experimental results to verify the performance of the proposed integrated model.

Resilience of the critical infrastructure in hospitals : Categorization and quantification as abasis for optimization

Critical infrastructure (CRITIS) in hospitals has become the focus of resilience research due to the impact of the COVID-19 pandemic and also the events in Ukraine. This foundational research examines overall contexts, categorizing and quantifying them. Previous research examined limited scale damage situations with little CRITIS involvement: Worst case studies are missing. The vulnerabilities of the CRITIS of one or more countries will likewise be aprime target for attack in current and future conflicts or criminal extortion, this is especially true in the healthcare sector. Therefore, detailed research with ablack swan scenario is necessary in this field. The aim of the study was to create and validate acategorized and weighted model for the self-assessment of the resilience of critical infrastructure in German hospitals at different levels of care before the exemplary scenario of aprolonged supraregional power blackout. Using an explorative design, experts from 8hospitals of different care levels performed an expert-based qualitative system analysis to develop, weight and test the model. The resilience index was then calculated using adapted interdependence analyses in aVester influence matrix. A total of 7 categories and 24subcategories of hospital CRITIS were identified. There are several key elements: rank1 of active elements is the emergency power system (E1), and for passive elements, it is the nursing staff (P2). This means that the emergency power system has the greatest impact on all other areas and the nursing staff are most dependent on all others for their work. The most critical elements, because they are most intertwined in the overall system, are the situation center/command staff (Z1) and technical staff (P3), on which the entire system depends. From the weighted individual elements of CRITIS, an overall resilience for ahospital can be calculated (resilience index). The developed model can be used by hospital crisis experts as part of aself-assessment to provide abasis for risk management, financial planning, technical planning, personnel planning or crisis and disaster management. The categorization and quantification of critical infrastructure (CRITIS) in hospitals with the aim of resilience documentation and optimization is possible. The model that has been developed allows rapid adaptation to changing initial situations and increases in resilience that can be realized in the short and medium term. Emergency and crisis preparedness is adynamic process, which has been combined here with the further development of critical infrastructure. Consequently, there can be no final state to be achieved but only acertain best possible framework within which the hospital as abusiness enterprise can operate. The classification of the categories in the model must also be constantly further developed and adapted to the current status. Once the explorative and qualitative basic research has been completed, it is necessary in afurther step to subject the model, which has been validated by experts, to abroader review. Ideally, this will be done using quantitative methods and asignificantly larger sample.

Simple Chargers for a Small DC Micro-Grid for a Home Emergency Power System

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies.

An athlete–referee dual learning system for real-time optimization with large-scale complex constraints

Constrained optimization (CO) has made a profound impact in solving many real-world problems. Due to the high computation burden in exact solvers, data-driven CO based on machine learning techniques is recently receiving extensive research interests for its capability to solve CO problems in real time. The existing data-driven CO approaches only serve for optimization problems with rather simple constraints that can be directly incorporated into model training. However, constraints that are computationally infeasible or burdensome to evaluate are commonly experienced in realistic optimization applications, especially in the engineering sector. This paper proposes an athlete–referee dual learning system (ARDLS) for end-to-end CO with large-scale complex constraints, where an athlete model is trained as the main optimizer while a referee model is trained as a probabilistic constraint classifier to guide the athlete training. A risk-based constrained loss function is designed to fine-tune the athlete model for constraint satisfaction. A case study on electric power system emergency control application is conducted to validate the proposed ARDLS, where the testing results demonstrate the excellent capability of ARDLS to improve the likelihood of satisfying large-scale complex constraints in CO.

Emergency load shedding strategy for high renewable energy penetrated power systems based on deep reinforcement learning

Traditional event-driven emergency load shedding determines the quantitative strategy by simulation of a specific set of expected faults, which requires high model accuracy and operation mode matching. However, due to the model complexity of renewable power generators and fluctuating power generation, traditional event-driven load shedding strategy faces the risk of mismatching in high renewable energy penetrated power systems. To address these challenges, this paper proposes an emergency load shedding method based on data-driven strategies and deep reinforcement learning (RL). Firstly, the reason for the possible mismatch of the event-driven load shedding strategy in the renewable power system is analyzed, and a typical mismatch scenario is constructed. Then, the emergency load shedding strategy is transformed into a Markov Decision Process (MDP), and the decision process’s action space, state space, and reward function are designed. On this basis, an emergency control strategy platform based on the Gym framework is established for application of deep reinforcement learning in the power system emergency control strategy. In order to enhance the adaptability and efficiency of the RL intelligence agent to multi-fault scenarios, the Proximal Policy Optimization (PPO) is adopted to optimize the constructed MDP. Finally, the proposed reinforcement learning-based emergency load shedding strategy is trained and verified through a modified IEEE 39-bus system. The results show that the proposed strategy can effectively make correct strategies to restore system frequency in the event-driven load shedding mismatch scenario, and have good adaptability for different faults and operation scenarios.

A Data-Driven Method for Power System Emergency Control to Improve Short-Term Voltage Stability

The power system contains a variety of uncertainties of different types of sources and loads, as well as random contingencies. Under these uncertainties and rapidly changing operating conditions, traditional rule-based methods cannot dynamically handle short-term voltage instability. To alleviate this situation, this paper proposes a novel power system emergency control scheme using a data-driven method, which combines edge-conditioned graph convolutional networks and deep reinforcement learning. The edge-conditioned graph convolutional network is utilized to extract the characteristics from not only power system nodes but also transmission lines. Deep reinforcement learning is introduced to perform load shedding actions, so as to guarantee the safety and stability of the electric power system. The IEEE 39-bus network is utilized for simulations to validate the effectiveness of the proposed data-driven method. The outcomes demonstrate the proposed method can generate a superior strategy in a number of the short-term voltage instability(STVI) circumstances.

The Impact of Cyber Attack on Emergency Energy System

An emergency energy system is a backup power system used in critical situations with the aim of protecting lives and property from the consequences of energy loss, as is the case in hospitals (heart monitors, Ventilator,…etc), and also in sensitive facilities (military industries and government buildings). The emergency power system can contain batteries of all kinds, in addition to solar and wind energy equipment and other cheap types of energy. The concept of cyber security arose several decades after the invention of the computer. At first, there was no need for cyber security, as it was difficult for electronic attacks to occur, because access to computers was limited to specific numbers of users, as the devices were giant confined to a room with certain specifications and were not Linked to networks at the time, Energy sectors expose themselves to a range of cyber threats that can damage control systems. So management, engineering, and IT must adhere to a comprehensive approach that includes threat prevention, detection, and elimination. In this research we will try to mitigate the effects of the cyber attack on these systems by applying some algorithms in order to detect, identify and prevent such attacks, and we will see through the results we obtained that we have made remarkable progress in this field.

Reliability analysis of ram air turbine blade based on AK-MCS algorithm

The Ram Air Turbine is an important part of the aircraft emergency power and hydraulic system. In this paper, the simulation system of NACA-4418 airfoil, blade chord length and twist are established. Flow Simulation plug-in is used to carry out fluid Simulation analysis on the impeller system, the surface pressure, surface relative pressure, surface shear stress, surface fluid density and velocity of the impeller were analyzed respectively. After fluid simulation of the impeller, the surface pressure and relative pressure are obtained, and the static stress analysis of the impeller in the corresponding environment is further carried out to judge the reliability of its operation. Finally, the reliability of turbine blades is calculated based on AK-MCS algorithm. The results show that the surface pressure of the impeller is affected by the overall structure of the impeller and the number of blades. From the perspective of relative surface pressure, ‘three blades’, ‘five blades’ and ‘seven blades’ have better reliability. Finally, based on AK-MCS algorithm analysis and calculation, the five-blade impeller has the highest reliability.

A dynamic hybrid protection scheme for on-site emergency power system operated through an integrated microgrid during station blackout in NPP

Post Fukushima nuclear disaster, application of renewable energy operated integrated microgrid (IMG) into nuclear power plants for strengthening the station blackout (SBO) power supply becomes popular. The IMG powered emergency-SBO (EM-SBO) is a bidirectional power system which leads to substantial variation in fault current. On the other hand, as per the guidelines of nuclear regulatory authority, minimum protection scheme is to be provided for EM-SBO power system to avoid unnecessary tripping of the safety power system. This scenario is a challenging task and a serious concern to provide suitable protection mechanism. To address this issue, a dynamic hybrid protection (DHP) scheme (hybrid differential and adaptive overcurrent protection scheme) is proposed. The proposed DHP scheme has been validated in EMTP-rv and DigSILENT Power Factory simulation software followed by harmonics analysis through Simulink. Finally, the superiority of DHP scheme for IMG has been established.

Capacity evaluation of central air conditioning load participating in peak shaving of renewable dominated power systems

The development of renewable-dominated power systems (RDPS) in the context of pursuing deep decarbonisation targets necessitates suitable exploitation of the peak shaving capacity (PSC) of central air conditioning loads (CACL) in multiple time scales. Based on the evaluation framework of CACL participating in the RDPS PSC and the CAC apparent temperature model and its operation mode, the CACL PSC evaluation model related to the resource allocation in multiple time scales is proposed. For the time scale of online dispatching, the capacity evaluation model for rapid CACL response to power system emergency peak shaving command and its continuous response potential is constructed. The method of optimising CACL PSC based on the load aggregator model is developed. For the intraday pre-scheduling time scale, the evaluation model of CACL's remaining capacity after undertaking the peak shaving task and the ability to respond continuously to the peak shaving command is constructed. For the day-ahead dispatching time scale, the PSC of CACL under different weather conditions, temperatures, holidays, and other typical scenarios is evaluated. Finally, case studies carried out on 14 central air-conditioned buildings in Shanghai city comprehensively validate the effectiveness of the proposed CACL PSC evaluation model.

Adaptive state of energy evaluation for supercapacitor in emergency power system of more-electric aircraft

This paper presents an adaptive state of energy (SOE) evaluation method for a supercapacitor in the emergency power system of a more-electric aircraft. The adaptive SOE evaluation is realized based on a dynamic first-order RC equivalent circuit and its self-adaptive updated parameters. First, a dynamic first-order RC equivalent circuit with three parameters (i.e., the charge/discharge resistance R0, the dynamic self-discharge resistance R1, and the dynamic capacity C1) and a state-space model are introduced for modeling the supercapacitor. Then, an adaptive sliding mode observer is designed for tracking the dynamic change of the three state-space model parameters (i.e., R0, (R0+R1)/R1C1, and 1/R1C1). With this design, the three parameters of the dynamic first-order RC equivalent circuit can be updated adaptively along with the derivation of the three state-space model parameters. Moreover, the adaptive sliding mode observer is redesigned with a sign function, which can enhance the convergence rate of the three self-adaptive updated parameters. Finally, the accurate SOE evaluation would be achieved based on the three self-adaptive updated parameters. Various simulations and experiments are conducted to validate the adaptive SOE evaluation method. It is shown that the adaptive SOE evaluation method can achieve 0.5% of the maximum relative error of the estimated SOE. Compared with the commonly used method, the adaptive SOE evaluation method has over 1% improvement for the SOE estimation accuracy.

Application of knowledge graph in power system fault diagnosis and disposal: A critical review and perspectives

Knowledge graph (KG) has good knowledge expression ability and interpretation, and its application to power system fault diagnosis and disposal can effectively integrate data of the whole life cycle of equipment and form a novel knowledge-driven operation and maintenance management mode. This is crucial to assist dispatchers in fault disposal and effectively improve the power system emergency handling capability and dispatch intelligence level. This paper conducts a systematic review and summary of the application of KG in power system fault diagnosis and disposal, so as to provide an adequate and comprehensive guide for further research in this field. Firstly, the definition, status, and classification of KGs are systematically described, and the general process of KG construction is sorted out. Secondly, the basic framework, construction process, key technologies, and typical practices of KG for power system fault diagnosis and disposal are summarized and reviewed in detail. Ultimately, several challenges, opportunities, and perspectives of KG in this field are carefully presented.

Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health. Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, information-seeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.

Modelling and control of central air‐conditioning loads for power system emergency frequency control

The physical model as well as the corresponding parameter identification method of central air conditioning load is proposed in this paper, based on which emergency control strategy of central air conditioning is designed to participate in emergency frequency control after the sudden large power loss in receiving end grid such as high voltage direct current (HVDC) blocking fault. Firstly, the basic principle of the central air conditioner is introduced. On this basis, the physical model of the central air conditioner which includes central air conditioning unit, temperature change of refrigerated inflow and outflow water, heat exchange between the cooling water of the fan coil and the room, indoor average temperature change as well as the proportion of the room in the open state are established. Moreover, combined with the central air-conditioning model, a parameter identification method of the model is proposed. Then, the emergency control and flexible recovery strategy of central air-conditioning are proposed. The application prospect of emergency control of central air conditioning in response stability control and low frequency load shedding (UFLS) after HVDC blocking faults of receiving power grid is discussed, respectively. The experimental platform is set up to verify the accuracy of the model and parameter identification method and the feasibility of emergency control.