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Based on the practical problem that excessive effluent pollutants caused by the impact of soybean wastewater on urban sewage treatment system, the effect of soybean wastewater on pollutants removal in sequencing batch reactor (SBR) and emergency control measures were investigated through single cycle instantaneous wastewater impact test. The results showed that with the increase of water quality impact load, COD removal rate increased initially and then decreased, while NH4+-N and TN removal rates increased gradually. The effect of water quality impact on TP removal was relatively small. When COD of the impact wastewater was 370 mg/L, the effluent NH4+-N and TN could not meet the first level class A standard of Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant (GB 18918-2002). As the water volume impact increased, the changes of COD and TN removal rates remained consistent with the water quality impact, and the effect of water volume impact on NH4+-N and TP removal was relatively small. As the impact of water quality and water volume increased, the protein(PN) and polysaccharide(PS) components in extracellular polymeric substances(EPS) were both affected. Regulating the aeration rate could be used as an effective emergency control measure for the impact of soybean products wastewater. When the COD of the impact wastewater was 370 mg/L and the aeration rate was 0.9 L/min, COD, NH4+-N, TN, and TP could all meet the discharge standards. The results could provide reference for coping with the impact of other loaded soybean products wastewater.

Emergency control is an essential means to help system maintain synchronism after fault clearance. Traditional “offline calculation, online matching” scheme faces significant challenges on adaptiveness and robustness problems. To address these challenges, this paper proposes a novel closed-loop framework of transient stability prediction (TSP) and emergency control based on Deep Belief Network (DBN). First, a hierarchical real-time anti-jitter TSP method using sliding time windows is adopted, which takes into account accuracy and rapidity at the same time. Next, a sensitivity regression model is established to mine the implicit relationship between power angles and sensitivity. When impending instability of the system is foreseen, optimal emergency control strategy can be determined in time. Lastly, responses after emergency control are fed back to the TSP model. If prediction result is still unstable, an additional control strategy will be implemented. Comprehensive numerical case studies are conducted on New England IEEE 39-bus system and Northeast Power Coordinated Council (NPCC) 140-bus system. Results show the proposed method can detect instability of system as soon as possible and assist in maintaining reliable system synchronism.

Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles.

Wheel failure is one of the worst problems for a planetary rover working on Mars or the Moon, which may lead to the interruption of the exploration mission and even the loss of mobility. In this study, a driving test of a planetary rover prototype with a faulty drive wheel was conducted, and state analysis and dynamics modeling were carried out. The drag motion relationship between the faulty drive wheel and the normal wheels on the same suspension was established based on the targeted single wheel test (faulty wheel-soil bin). In order to maintain the subsequent basic detection capability of the planetary rover, an emergency control system is proposed that integrates the path planning strategy with faulty wheel priority and the motion control method of correcting heading and coordinating allocation. The experimental results and emergency strategies of this study on simulating Martian soil and terrain can provide researchers with ideas to solve such problems.

Abstract Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios.

This paper proposes a probabilistic energy and reserve co-dispatch (PERD) model to address the strong uncertainties in high-renewable power systems. The expected costs of potential renewable energy curtailment/load shedding are fully considered in this model, which avoids insufficient or excessive emergency control capacity to produce more economical reserve decisions than conventional chance-constrained dispatch methods. Furthermore, an analytical reformulation approach of PERD is proposed to make it tractable. We firstly develop an approximation technique with high precision to convert the integral terms in objective functions into analytical ones. Then, the calculation of probabilistic constraints is equivalently transformed into an unconstrained optimization problem by introducing value-at-risk (VaR) representation. Specifically, the VaR formulas can be computed by a computationally-cheap dichotomy search algorithm. Finally, the PERD model is transformed into a convex problem, which can be solved reliably and efficiently using off-the-shelf solvers. Case studies are performed on IEEE test systems and real provincial power grids in China to illustrate the scalability and efficiency of the proposed method.

Communication failures and transmission delays are two major issues associated with Wide-Area Damping Controllers (WADCs). While transmission delays have been extensively studied and various solutions have been proposed, little research has been done on communication failures and most of the proposed methods are based on preventive controls. However, in today’s liberalized electricity markets, preventive controls are no longer acceptable and the trend is to use emergency controls instead. This paper proposes a novel emergency control scheme to counteract the loss of remote signals related to the input and to the output of the WADC (i.e., sensor and actuator failures). The proposed scheme is based on a simple criterion, which overcomes the complexity of the previous methods. Modal analysis and time domain simulations are performed to verify the performance of the proposed method. The simulation results show that the proposed method performs well in handling communication failures and can maintain good damping performance. This research work is particularly important in view of the trend towards the wide-scale adoption of wide-area measurement technologies, while the vulnerability to cyber-attacks is increasing.

Abstract Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre‐defined data. The purpose of this study is to develop an adaptable algorithm for turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator “torque–load angle”. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre‐defined parameters of a power system model are required. The suggested algorithm was tested on the power system model NE39bus using Matlab/Simulink. The efficiency of the suggested algorithm is verified and demonstrated.

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

This paper discusses the construction method of ventilation and prevention system based on the application practice of Zhuanlongwan Coal Mine. A precise perception technology for key parameters of ventilation network is proposed. Through the optimal arrangement of sensors and the use of the least monitoring points, the correlation parameter analysis of the whole mine ventilation resistance route is achieved; the remote software modules of the main ventilator, local ventilator, and ventilation facilities are developed by extracting the ventilation and prevention information of the safety monitoring system, and the modules such as beam pipe monitoring, dust monitoring, grouting monitoring, and personnel positioning system are integrated; the intelligent analysis technology for the state of the ventilation and prevention system is proposed, and the three-dimensional simulation model of the mine ventilation network is established, which achieves the real-time calculation of the ventilation network, the visual display and trend analysis of the ventilation and prevention parameters, and the diagnosis and early warning of ventilation anomalies. By organically integrating ventilation, gas, fire, dust and other ventilation and prevention safety information, a hierarchical management and control mode of mine ventilation and prevention system based on multi heterogeneous information integration is proposed, a mine intelligent ventilation and prevention platform is developed, and mine emergency management and control platform is invented. Through field practice, the relevant parameter judgment and control model are verified, which provides application experience for the construction of mine intelligent ventilation and prevention system.

Modern electrical power systems place special demands on the speed and accuracy of transient and steady-state process control. The introduction of renewable energy sources has significantly influenced the amount of inertia and uncertainty of transient processes occurring in energy systems. These changes have led to the need to clarify the existing principles for the implementation of devices for protecting power systems from the loss of small-signal and transient stability. Traditional methods of developing these devices do not provide the required adaptability due to the need to specify a list of accidents to be considered. Therefore, there is a clear need to develop fundamentally new devices for the emergency control of power system modes based on adaptive algorithms. This work proposes to develop emergency control methods based on the use of deep machine learning algorithms and obtained data from synchronized vector measurement devices. This approach makes it possible to ensure adaptability and high performance when choosing control actions. Recurrent neural networks, long short-term memory networks, restricted Boltzmann machines, and self-organizing maps were selected as deep learning algorithms. Testing was performed by using IEEE14, IEEE24, and IEEE39 power system models. Two data samples were considered: with and without data from synchronized vector measurement devices. The highest accuracy of classification of the control actions’ value corresponds to the long short-term memory networks algorithm: the value of the accuracy factor was 94.31% without taking into account the data from the synchronized vector measurement devices and 94.45% when considering this data. The obtained results confirm the possibility of using deep learning algorithms to build an adaptive emergency control system for power systems.

The combined usage of phasor measurement units and machine learning algorithms provides the opportunity for developing response-based wide-area system integrity protection scheme against transient instability in power systems. However, only the transient stability status is usually predicted in the literature, which is not enough for real-time decision-making for response-based emergency control. In this paper, an integrated approach is proposed. The GRU-based predictor is firstly proposed for post-disturbance transient stability prediction. On this basis, a multi-task learning framework is proposed for the identification of unstable machines and also the estimation of generation shedding. Case study on the IEEE 39-bus system demonstrates that, apart from the basic task of transient stability prediction, the proposed GRU-based multi-task predictor can predict the grouping of unstable machines correctly. Moreover, based on the estimated amount of generation shedding, the generated remedial control actions can retain the synchronism of the power system.

The operation of transmission systems with large share of wind power is specially challenging under storm conditions. Under the stormy wind speed conditions, wind turbine protection system is designed to shut down the turbine to avoid excessive mechanical load. The sudden loss of wind power from large offshore plants is difficult to forecast accurately, which results in a large amount of power imbalance. The severity of such a wind power imbalance towards frequency stability needs to be studied for the future power systems. In addition, the overhead transmission lines can also be affected during storms, thereby increasing their probability of failure in the operation of power system under the islanded conditions. This paper investigates how the stormy weather can threaten the frequency stability of future Danish power system with large share of wind power and how to avoid the frequency instability through proper control and defence strategies such as high-voltage direct current (HVDC) control and load shedding. Sensitivity studies are performed for ramp rates of HVDC control, load shedding strategies, inertia of the system with different volumes of disturbances to understand their impact on frequency stability.

The real-time transient stability assessment (TSA) and emergency control are effective measures to suppress accident expansion, prevent system instability, and avoid large-scale power outages in the event of power system failure. However, real-time assessment is extremely demanding on computing speed, and the traditional method is not competent. In this paper, an improved deep belief network (DBN) is proposed for the fast assessment of transient stability, which considers the structural characteristics of power system in the construction of loss function. Deep learning has been effective in many fields, but usually is considered as a black-box model. From the perspective of machine learning interpretation, this paper proposes a local linear interpreter (LLI) model, and tries to give a reasonable interpretation of the relationship between the system features and the assessment result, and illustrates the conversion process from the input feature space to the high-dimension representation space. The proposed method is tested on an IEEE new England test system and demonstrated on a regional power system in China. The result demonstrates that the proposed method has rapidity, high accuracy and good interpretability in transient stability assessment.

Stochastic renewable sources of energy have been causing changes in the structure and operation of power systems. High penetration of this type of generation results in decreased inertia of a power system, increased active power fluctuations, and a higher probability of false tripping of emergency control devices. Traditional algorithms of emergency control are not adaptable and flexible enough for systems with high penetration of renewables and flexible alternating current transmission systems. Integration and development of phasor measurement units make it possible to create adaptable emergency control systems, which would require minimal pre-defined data. The purpose of this study is to develop an adaptable algorithm of turbine fast valving control synthesis and transient stability estimation for a generator. The suggested algorithm is based on the equal area criterion in the domain synchronous generator Torque—Load angle. The measurements of the generator operation under consideration are used as the input data for the steam turbine fast valving control synthesis. Thus, the algorithm becomes adaptable because no pre-defined parameters of a power system model are required.

In the event of accidents in power systems associated with a reduction in frequency in order to prevent the complete repayment of the energy district and accelerate the elimination of the accident, frequency division automatics (FDA) are used. Existing technical solutions for the implementation of the FDA do not always provide a balance of the generated and consumed power under the action of the FDA, since the current circuit-mode situation is not taken into account. The aim of this work is to develop effective algorithms for the functioning of adaptive FDA, which allocates power units of a power plant to a balanced load of an isolated energy district, taking into account the current circuit-mode situation. To achieve this goal, we use the method of structural and parametric synthesis of emergency control algorithms, which consists in creating a composite algorithmic model of adaptive FDA. The result of this work is the developed algorithmic model of an adaptive FDA of a power plant, consisting of input and processing algorithms for input analog signals of a FDA, algorithms for input and processing of input discrete signals of a FDA, algorithms for generating start-up organs of a FDA, algorithms for allocating a power plant to a balanced load of a power district. The proposed algorithm for the functioning of adaptive FDA is universal and allows you to automatically allocate power units of a power plant to a balanced load of an isolated energy district, regardless of the type of accident and its cause, the configuration of the electrical network and the values of electricity generation and consumption, as well as the current circuit-mode situation.

The purpose of the article is to study the possibility and feasibility of participation of nuclear power plants (NPPs) with VVER in emergency frequency control in power systems with a high proportion of nuclear power units and, at the same time, of reducing the power consumption for the own needs of the main circulation pumps during modes with power below nominal. To solve these problems, it was proposed to increase the achievable speeds of power gain (load increase) due to the installation of frequency controlled drives of the MCP. Large system frequency variations (caused by large imbalances between generation and demand) may jeopardize electrical equipment, in terms of maintaining stable and reliable operating conditions. For NPPs, the task of preventing or localizing accidents is even more important than for TPPs, since in case of major system accidents, it is possible to completely stop external power supply of the NPPs own needs. Thus, besides the requirements for the primary control of the frequency of NPPs with VVER, today we need more stringent requirements for their emergency acceleration and mobility. The operation of NPPs with long-term non-recoverable active power shortage causes a decrease in the speed of the main circulation pumps of NPPs with VVER and a decrease in the coolant flow rate. It is shown that the installation of variable frequency drives of the MCPs at NPP with VVER is appropriate not only to save energy consumption for their drive in partial modes, but also to increase the power of NPP above the nominal (without reducing the reserve before the heat exchange crisis in the reactor core) for the elimination of system accidents, and thus to improve the safety of the NPPs included in the power system.

As a typical form of integrated energy system, micro energy system (MES) is promoted and popularized at the terminal of distribution grid. To achieve the efficient and reliable energy utilization of MES, two main problems need to be solved: how to consider the dynamic characteristics of heterogeneous energy resources and diverse MES operating states under multiple time scales and how to coordinate the interests of both MES operator and distribution grid operator. This paper aims to establish the models of specific energy devices in MES and propose a coordinated control strategy for multi MESs within distribution grid considering dynamic characteristics and contradictory interests. The proposed strategy is presented from the MES side and distribution grid side with consideration of different control objectives under day-ahead dispatch, intra-day dispatch and emergency control circumstances. A case study based on IEEE 33-bus system with MESs connected is conducted to verify the effectiveness of the proposed strategy.

The widespread application of phasor measurement units has improved grid operational reliability. However, this has increased the risk of cyber threats such as false data injection attack that mislead time-critical measurements, which may lead to incorrect operator actions. While a single incorrect operator action might not result in a cascading failure, a series of actions impacting critical lines and transformers, combined with pre-existing faults or scheduled maintenance, might lead to widespread outages. To prevent cascading failures, controlled islanding strategies are traditionally implemented. However, islanding is effective only when the received data are trustworthy. This paper investigates two multi-objective controlled islanding strategies to accommodate data uncertainties under scenarios of lack of or partial knowledge of false data injection attacks. When attack information is not available, the optimization problem maximizes island observability using a minimum number of phasor measurement units for a more accurate state estimation. When partial attack information is available, vulnerable phasor measurement units are isolated to a smaller island to minimize the impacts of attacks. Additional objectives ensure steady-state and transient-state stability of the islands. Simulations are performed on 200-bus, 500-bus, and 2000-bus systems.

This paper proposes a design methodology for a six-wheeled rover that adapt to different stairs and maintain its stability based on the robot’s parameters, the kinematics constraints, the maximum height, and the minimum length of the step required to climb up and down. We also propose an emergency controller to prevent falls during the climb up or down using the contact angle measurement between wheel–ground by a laser scanner sensor on each side of the robot. Thus, the geometry terrain information and the wheel contact loss detection with the ground can be obtained. This loss of contact with the ground is a determining factor in an emergency case where the robot’s stability is at risk. Therefore, the controllers kick in to regain the wheel contact with the step, preventing the robot from falling. Simulations and experimental results when the robot climbs up and down stairs demonstrate the ability to react to possible falls.

Power system transient stability control based on response is one of the main applications of wide-area measurement system (WAMS). WAMS that can obtain real-time dynamic response data of the whole power grid, provides a strong technical support to the responsebased power system transient stability. The key technologies of the response-based power system transient stability are summarized, mainly including three aspects, i.e., post-fault trajectory prediction of transient stability, transient stability assessment, transient stability emergency control. The disadvantages of the existing methods were presented, as well as the characteristics of the response-based transient stability control technology were summarized, and on this basis the key problems to be solved in the future were pointed out.

Line overload is one of the important causal factors of cascading failures and blackouts in power systems. An optimization method for protection and control measures to mitigate line overloads is proposed in this study. The method consists of two main parts, i.e., the modeling process and the solving process. In the modeling process, an optimization model including overload protection and emergency control measures is developed using PFT (Power Flow Tracing). In the solving process, a multi-stage optimization method using IBSO (Improved Brain Storm Optimization algorithm) is proposed to obtain the final result. The aim of this study is to form a coordinated protection and control strategy that reduces the power on the overloaded line within the safety limits and minimizes the load loss of the power system. The simulation results show the effectiveness of the proposed method.

Maritime management is a crucial concern for movable bridge safety. Irregular management of water vehicles near movable bridges may lead to collision among ships and bridge infrastructures, causing massive losses of life and property. The paper presents a theoretical framework and simulation of an intelligent water vehicle management system for movable bridges corresponding to vehicle traffic responses. The water regime around the bridge is considered in virtually separated domains to estimate the desired safety actions based on the position of the approaching ships. An emergency clash avoidance control system is represented to prevent ship-infrastructure collision and ensure transportation safety. In addition, a simulation platform is developed specifically adaptable for movable bridge maritime and dynamic traffic management. The proposed theory is experimented using the simulation platform for different ship speeds and bridge-vehicle traffic volumes. Based on analyzing the velocity profile of approaching ships at different incidents, the bridge is found incapable of evacuating vehicles and unable to open promptly in case of speeding ships and high traffic density of vehicles on the bridge. Computational results show that the emergency control system is effective in reducing ship speed and prevent certain collisions. Lastly, the transportation policy for the newly proposed maritime management system is validated by real-world implementation in movable bridges across the world.

Small-scale power generation based on renewable energy sources is gaining popularity in distribution grids, creating new challenges for power system control. At the same time, remote consumers with their own small-scale generation still have low reliability of power supply and poor power quality, due to the lack of proper technology for grid control when the main power supply is lost. Today, there is a global trend in the transition from a power supply with centralized control to a decentralized one, which has led to the Microgrid concept. A microgrid is an intelligent automated system that can reconfigure by itself, maintain the power balance, and distribute power flows. The main purpose of this paper is to study the method of control using reclosers in the Lahsh district of the Rasht grid in Tajikistan with distributed small generation. Based on modified reclosers, a method of decentralized synchronization and restoration of the grid normal operation after the loss of the main power source was proposed. In order to assess the stable operation of small hydropower plants under disturbances, the transients caused by proactive automatic islanding (PAI) and restoration of the interconnection between the microgrid and the main grid are shown. Rustab software, as one of the multifunctional software applications in the field of power systems transients study, was used for simulation purposes. Based on the simulation results, it can be concluded that under disturbances, the proposed method had a positive effect on the stability of small hydropower plants, which are owned and dispatched by the Rasht grid. Moreover, the proposed method sufficiently ensures the quality of the supplied power and improves the reliability of power supply in the Lahsh district of Tajikistan.