Emergency Control Strategy Research Articles

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

SE-CNN based emergency control coordination strategy against voltage instability in multi-infeed hybrid AC/DC systems

For receiving-end power systems with multi-infeed HVDCs and large-scale renewable energy generation, the challenges of weak reactive power support and limited anti-interference capacity pose a threat to the power system voltage stability. To enhance the voltage stability under large disturbances, this paper proposes a data-driven method for coordinated emergency control strategy against voltage instability in multi-infeed hybrid AC/DC systems based on the squeeze and excitation (SE) module and convolutional neural network (CNN). Firstly, the SE module and CNN are integrated to intuitively capture correlations among various feature channels and extract key features related to voltage stability levels. Secondly, a voltage stability evaluation method is proposed based on the SE-CNN model, utilizing a dual channel structure to unveil the quantitative relationship between key response characteristics and voltage stability margin. Finally, regarding the control measures containing load shedding and DC modulation, the emergency control sensitivity index is proposed to assess the stability margin improvement effect of various control objects, and the optimal emergency control strategy is formulated to coordinate multiple voltage stability emergency control measures. Case studies were conducted on a typical China local region receiving-end power grid test system with voltage instability problems, and the simulation results verified the effectiveness of the proposed method. © 2017 Elsevier Inc. All rights reserved.

Emergency Control Strategy of Hybrid Advanced Traction Power Supply System Based on Multidevice Collaboration

Emergency Control Strategy of Hybrid Advanced Traction Power Supply System Based on Multidevice Collaboration

Cooperative Decision-Making Approach for Multiobjective Finite Control Set Model Predictive Control Without Weighting Parameters

Finite control set model predictive control (FCS-MPC) has gained increasing popularity as an emerging control strategy for electrical drive systems. However, it is still a challenging task to optimize weighting parameters, as multiple objectives are involved in the customized cost function. A cooperative decision-making approach for FCS-MPC is proposed in this article, to solve the optimization problems with manifold control objectives. By splitting the cost function, the optimization problem underlying multi-objective FCS-MPC is separated into a series of decomposed optimization problems. By doing so, the dimension of the decomposed problem is reduced to one. To collect the information for decision-making, an efficient sorting algorithm is applied for each control objective. The theory behind the cooperative decision-making approach is comprehensively analyzed, to validate both the effectiveness and efficiency of the proposed scheme. More specifically, the highlight is the adaptive mechanism on the number of desired candidates, to obtain a decent performance for torque and flux. The candidate which minimizes the switching frequency is selected as the optimal. The proposed scheme is experimentally verified and compared with the existing FCS-MPC without weighting parameters.

Frequency emergency control strategy in power systems considering the participation of energy storage clusters

Recently, the power systems with a high penetration of renewables and power electronics have come into being. In these power systems, complex system dynamics, emergency faults, and insufficient frequency regulation reserve pose threats to system frequency stability. Based on the clustering development of energy storage, to ensure the system frequency stability when emergency faults occur, this paper proposes a decentralized frequency emergency control (FEC) strategy considering the participation of energy storage clusters (ESCs). First, the overall framework of the optimal-droop-based FEC strategy is introduced, which achieves the coordination between FEC and conventional frequency regulation strategies. Second, to appropriately allocate the unbalanced power among the generators and ESCs, a general design method for optimal droop coefficients is proposed, which is applicable to various control objectives. The optimality of the droop coefficients is rigorously proven. This case study is carried out on an electromagnetic transient simulation platform, and the simulation results verify the effectiveness and optimality of the proposed FEC strategy.

Data-Driven Emergency Frequency Control for Multi-Infeed Hybrid AC-DC System

With the continuous development of large-scale complex hybrid AC-DC grids, the fast adjustability of HVDC systems is required by the grid to provide frequency regulation services. This paper develops a fully data-driven linear quadratic regulator (LQR) for the HVDC to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of multi-infeed hybrid AC-DC (MIDC) systems dynamics that make the LQR intractable. Based on Koopman operator (KO) theory, a Koopman eigenpairs construction method is developed to fit a global linear dynamic model of MIDC systems. Once globally linear representation of uncontrolled system dynamics is obtained offline, the control term is constituted by the gradient of the identified eigenfunctions and the control matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{B}$</tex-math></inline-formula> . In case that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{B}$</tex-math></inline-formula> is unknown, we propose a method to identify it based on the verified Koopman eigenfunctions. The active power reference is optimized online for LCC-HVDC in a moving horizon fashion to provide frequency support, with only locally measurable frequency and transmission power. The robustness of the proposed control method against approximation errors of the linear representation in eigenfunction coordinates is analyzed. Simulation results show the effectiveness, robustness and adaptability of the proposed emergency control strategy.

Emergency Control Strategy for Electrolytic Aluminum Load Considering the Load's Transient Overcurrent Characteristics and Transient Voltage Stability Constraints

Emergency Control Strategy for Electrolytic Aluminum Load Considering the Load's Transient Overcurrent Characteristics and Transient Voltage Stability Constraints

A novel synchronized data-driven composite scheme to enhance photovoltaic (pv) integrated power system grid stability

The performance of power networks is enhanced by the penetration of solar energy, which helps to equate continuously the generation and demand power imbalance. However, the time margin that grids must adapt to unforeseen frequency fluctuations and restore generation-demand equivalency is reduced by these linkages. Consequently, it exerts the stability and performance of the power grid at risk. Thus, it becomes vital to assess real-time system data and to recognize and implement suitable remedies to maintain a healthy system performance. In order to improve grid stability in power networks that have solar energy penetration, this manuscript suggests a data driven integrated framework. The proposed approach is a two-step framework wherein the first stage assesses impending transient instability in the system through novel Instability Evaluation (IE). Step two involves creating and deploying a Decision Boundary based Control (DBC) to stabilize an unstable system following an emergency control strategy. An IE module employing short-synchronized movement data is presented for evaluating post-disturbance transient stability (TS). In the initial cycles following the fault initiation, the IE projects the impending transient instability. Next, an innovative DBC creates an emergency remedial system for unstable processes that determines the nature, magnitude and location of the remedial action. The DBC assesses pertinent action sets that it implements to sustain system stability using a proposed Decision Assisted Inference (DAI) technique. The simulation investigations validate the aptness of suggested analysis on the performance of power system with and without PV and topological variations.

Enhancing Performance of Andronov-Hopf Oscillator-Based Grid-Forming Converters in Microgrids With Non-Invasive Online Impedance Estimation

The Andronov-Hopf based Virtual Oscillator Control (AHO) is an emerging control strategy for grid-forming converters (GFC) to ensure the stability of the future power system with high level of penetration of renewable energies. AHO allows converters to operate in either grid-connected and islanded operating modes. However, the application of this control strategy is limited due to the inherent problem in simultaneous controlling active and reactive power output of GFCs. This paper presents a modification in AHO control structure, and adopts a wide-band grid impedance estimation to improve the dispatchability of AHO in grid-connected mode. The impedance estimation algorithm is embedded into the control loop of AHO, and requires only the measurement of the voltages and currents at the point of common coupling. Thus it is cost-effective and practical. The performance of the proposed method is validated in both simplestructure, and large low-voltage benchmark distribution systems, using MATLAB/Simulink software and the real-time Opal-RT platform. The simulation results confirm the high-accuracy of the estimation and the capability of AHO in simultaneously controlling active and reactive power with zero steady-state errors. Additionally, this paper discusses the small-signal stability and Lyapunov large signal stability of the proposed AHO and compares it to existing methods.

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

AbstractThe occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.

Active Emergency Control Strategy for Wind Farms Considering Fatigue Load and Action Damage

With the increase of renewable energy in power systems, the number of traditional coal power plants is decreasing year by year. The emergency control by tripping off the generators is no longer sufficient to maintain the stability of the system. Aiming to solve the problem with the help of wind farms, a cutting method based on reducing fatigue and motion damage is proposed. Firstly, FCM is used to select machines suitable for removal from the wind farm; then, an optimization model to minimize mechanical loss and output error is constructed by analyzing the shutdown process, and the cutter solution is obtained by solving the 0-1 planning problem; finally, the effectiveness of the proposed method is verified by example analysis.

Transient voltage stability emergency control strategy for HVDC receiving end power grid based on global orthogonal collocation

When asynchronous motors, especially double-fed asynchronous motors in large capacity pump storage are the main loads in the high voltage direct current (HVDC) receiving end power grid, the increase of the equivalent slip of asynchronous motor load may cause transient voltage instability. In order to recover the voltage rapidly in the grid, the emergency reactive power support needs to be quick and accurate. A method for transient voltage stability emergency control by temporarily reducing DC current is proposed, the inverter station is used as emergency reactive power source for the HVDC receiving end power grid. In detail, firstly, aiming at the quantitative calculation of DC current, a nonlinear optimization model with the optimization variable of DC current and the objective of minimizing energy transmission reduction of HVDC is established. Further, in order to achieve fast solution and meet the accuracy requirements, global orthogonal collocation (GOC) is incorporated into the optimization model to transform the differential equations of both objective function and constraints into algebraic equations, thus the optimization is transformed into a nonlinear programming (NLP) problem, by which the emergency control strategy, in specific, the optimal DC current control scheme is obtained. Finally, the modified IEEE 14 benchmark is used to verify the effectiveness and superiority of the proposed strategy.

Proximal policy optimization through a deep reinforcement learning framework for remedial action schemes of VSC-HVDC

A proximal policy optimization (PPO)-based back-to-back VSC-HVDC emergency control strategy based on multi-agent deep reinforcement learning (DRL) approach is proposed for use in an energy management system (EMS). In this scheme, an advanced DRL algorithm is proposed by implementing both PPO and a communication neural network for large power systems. The PPO modeled as intelligent agents with objective functions have shown a higher convergence performance than have existing DRL algorithms. Further, the model was demonstrated to effectively address voltage variances caused by the high penetration of renewable energy sources. By implementing PPO, the learning procedure is stabilized and made robust to continuous changes in network topology. To escalate the effectiveness of the proposed algorithm, a comprehensive case studies were conducted on an standard test systems and Korean power system considering variations in load and PV generation and a weak centralized communication environment. The results indicate that outstanding control performance and autonomously regulated bus voltage and line flows, thereby validating the effectiveness of the method.

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.

Biofilm Formation and Control of Foodborne Pathogenic Bacteria.

Biofilms are microbial aggregation membranes that are formed when microorganisms attach to the surfaces of living or nonliving things. Importantly, biofilm properties provide microorganisms with protection against environmental pressures and enhance their resistance to antimicrobial agents, contributing to microbial persistence and toxicity. Thus, bacterial biofilm formation is part of the bacterial survival mechanism. However, if foodborne pathogens form biofilms, the risk of foodborne disease infections can be greatly exacerbated, which can cause major public health risks and lead to adverse economic consequences. Therefore, research on biofilms and their removal strategies are very important in the food industry. Food waste due to spoilage within the food industry remains a global challenge to environmental sustainability and the security of food supplies. This review describes bacterial biofilm formation, elaborates on the problem associated with biofilms in the food industry, enumerates several kinds of common foodborne pathogens in biofilms, summarizes the current strategies used to eliminate or control harmful bacterial biofilm formation, introduces the current and emerging control strategies, and emphasizes future development prospects with respect to bacterial biofilms.

Coordinated emergency control strategy of high‐voltage direct current transmission and energy storage system based on Pontryagin minimum principle for enhancing power system frequency stability

AbstractWith the increase of the proportion of renewable energy sources, the rotational inertia of the power system decreases, which results in the risk of frequency instability increasing. Based on Pontryagin minimum principle, this paper presents a systematic emergency control strategy by coordinating the active power of voltage source converter based high‐voltage direct current transmission (VSC‐HVDC) and energy storage system (ESS) to improve the system frequency stability, reduce the operation cost of VSC‐HVDC and ESS and implement energy management of ESS. In the stage of frequency fall, a hierarchical control strategy is proposed to prevent the frequency from dropping below the preset threshold value and achieve the optimal distribution of support power between VSC‐HVDC and ESS. For the system level, the total optimal power support trajectory of VSC‐HVDC and ESS is optimized by the Gaussian pseudospectral method with the minimum total control energy, considering the constraints of frequency. For the station level, the power references of VSC‐HVDC/ESS are determined by the model predictive controller (MPC), with the minimum frequency deviation of the non‐disturbed system connected to VSC‐HVDC, control cost of VSC‐HVDC and loss cost of ESS. In the stage of frequency recovery, an energy recovery control strategy is designed to aim at quickly recovering energy of ESS while considering the minimum frequency limit. Finally, the correctness and effectiveness of the proposed strategy are verified in the improved IEEE 39 bus system and a real‐power system.

An emergency control strategy for undervoltage load shedding of power system: A graph deep reinforcement learning method

AbstractUndervoltage 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.

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.

Simultaneous Planning and Execution for Safe Flight of Quadrotors suffering One Rotor Loss and Disturbance

Safety is crucial for quadrotor flight, especially when the system suffers one rotor loss and disturbance. To enable quadrotors to survive in these worst cases, this paper proposes a Simultaneous Planning And Execution (SPAE) system strategy. First, the Euler angles under different equilibrium states are solved to ensure the feasibility of the control input when the quadrotors encounters rotor loss and disturbance. Based on the admissible Euler angle solutions, an emergency control strategy is proposed to realize desired angles by recalculating the desired trajectory and the disturbance observer. Meanwhile, this control strategy sacrifices the yaw angle control to handle further underactuation of the system caused by complete rotor failure. In addition to rotor losses, unsafe flight caused by actuator saturation is considered in the SPAE strategy where the planning and execution are simultaneous. As a decision maker, the planning considers the current disturbances and constraints of actuators, then generates a feasible trajectory for controller execution. As an executor of the decision, the controller feedback estimated disturbance information to the trajectory planning, which can help trajectory planning make admissible decisions consistent with the current situation. As a result, the actuator saturation caused by the underestimation of disturbance can be avoided under a feasible regenerated trajectory. Extensive simulations and experiments prove the validity of the proposed SPAE strategy.

Accurate control of virtual oscillator-controlled islanded AC microgrids

Virtual oscillator control (VOC) is an emerging control strategy for grid-forming inverters. In contrast with the droop and virtual synchronous generator methods, VOC is a nonlinear and time-domain strategy that requires only the inverter output current measurement to control the inverter output. Hence, it is characterized by its good dynamic response and stable operation. To explore the VOC performance in islanded AC microgrids, this paper initially demonstrates for the first time the negative impacts of impedance mismatching of the interfacing feeders on power sharing among VOC-based inverters. Then, new control schemes to enhance the operation of VOC-based islanded AC microgrids are proposed. First, a fast and robust secondary control loop to restore voltage and frequency in the microgrid based on the adaptive tuning of the VOC voltage-scaling factor and the VOC inductance parameters is developed. Second, an optimal tuning approach of virdual complex impedance combines virtual inductance and resistance for each inverter is proposed to fully mitigate the impacts of impedance mismatch of the interfacing feeders. Consequently, accurate active and reactive power sharing among VOC-based inverters is achieved. Third, an online and non-invasive estimation technique for feeder impedance is embedded in the control loop of each inverter. Hence, prior knowledge of feeders’ impedances used to tune the virtual impedances of inverters is not required. Simulation and experimental results are presented to validate the efficacy of the proposed control scheme. • The negative impacts of impedance mismatch on power sharing are demonstrated. • A fast and robust secondary control loop for the VOC-based microgrid is developed. • An optimal tuning approach of the virtual impedance for each inverter is proposed. • The impacts of impedance mismatch of the interfacing feeders are fully mitigated. • An impedance estimation technique is embedded in the control loop of each inverter.