General Control System Research Articles

Starting Control of Free Piston Stirling Linear Generator System Based on FOC

Aiming at the problem of poor system dynamic performance caused by low parameter matching in the coordinated control of Stirling engine and linear generator in the starting stage control of free piston Stirling linear generator system, a joint control method of free piston Stirling permanent magnet synchronous linear generator system based on field orientation control is proposed, based on the theoretical derivation of the mathematical model of the system and the principle of controller parameters setting, the simulation experiments of the system starting stage under several Stirling engine working conditions are carried out under simulation. The experimental results show that the stability and rapidity of the system are improved, and the dynamic response speed of generator parameters under different working conditions is accelerated, what fully verifies the correctness and effectiveness of the method. It provides an effective way to improve the control performance of the system and stabilize the power generation operation.

Cognitive control, motivation and fatigue: A cognitive neuroscience perspective

The present article provides a unified systematic account of the role of cognitive control, motivation and dopamine pathways in relation to the development of fatigue. Since cognitive fatigue is considered to be one aspect of the general control system that manages goal activity in the service of motivational requirements (Hockey, 2011), our focus is also broader than fatigue itself.The paper shall therefore first focus on the motivation-control interactions at the level of networks of the brain. A motivational control network is argued to play a critical role in shaping goal-directed behavior, in conjunction with dopamine systems that energize the network. Furthermore, motivation-control interactions as implemented in networks of the brain provide an important element to elucidate how decision making weighs both the anticipated benefits and costs of control operations, in optimal and suboptimal conditions such as mental fatigue.The paper further sketches how fatigue affects the connectivity of large-scale networks in the brain during effortful exercition, in particular the high-cost long striatal-cortical pathways, leading to a global reduction of integration in the brain’s network architecture. The resulting neural state within these networks then enters as interoceptive information to systems in the brain that perform cost-benefit calculations.Based on these notions we propose a unifying cost-benefit model, inspired by influential insights from the current neuroscience literature of how fatigue changes the motivation to perform. The model specifies how the reward value, effort costs and fatigue aspects of task performance converge in the medial prefrontal cortex to calculate the net motivation value of stimuli and select the appropriate actions.

On the Existence and Computation of Minimum Attention Optimal Control Laws

One means of capturing the cost of control implementation of a general nonlinear control system is via Brockett's minimum attention criterion, defined as a multidimensional integral of the rate of change of the control with respect to state and time. Although shown to be important in human motor control and robotics applications, a practical difficulty with this criterion is that the existence of solutions is not always assured; even when they exist, obtaining local solutions numerically is difficult. In this paper we prove that, for the class of controls consisting of the sum of a time-varying feedforward term and a time-varying feedback term linear in the state, existence of a suboptimal solution can be guaranteed. We also derive a provably convergent gradient descent algorithm for obtaining a local solution, by appealing to the Liouville equation representation of a nonlinear control system and adapting iterative methods originally developed for boundary flow control. Our methodology is illustrated with a two degree-of-freedom planar robot example.

Fault-tolerant control system for once-through steam generator based on reinforcement learning algorithm

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

Estimation of the time for steam generator trip due to cyber intrusions

The time required to trip a pressurized water reactor (PWR) by inserting malicious signals into its steam generator (SG) control system has been studied using the Generic PWR (GPWR) Simulator. A semi-analytical model is developed to approximately reproduce the simulator response and understand the dynamics of the control unit. A series of two proportional-integral controllers determines control action according to preset constants, the readings from the feedwater level sensor, and those from feedwater and steam flowrate transmitters. It is observed that the most important factor that determines whether a trip will occur is how much additional water is added to or withheld from the SG over time compared to normal operating conditions. In order to determine the effects of control action on the SG, changes in mass inventory are considered. This approach models the SG water level as a function of mass inventory and has a backward temporal memory. A Python interface is developed for the GPWR framework to automatically simulate different spoofing scenarios and post-process the related data. We observe that the trip times predominantly depend on flow mismatch and/or level errors. Controller parameters, including the integral time and gain constants, either speed up or slow down the rate of progression to a trip setpoint but do not cause a trip by themselves. The reactor can trip on a high-level signal when the reading crosses above 78%, increased from its reference level of 57%, or a low-level reading when it is below 25%. The present results show roughly how long the operators would have to respond to an attack, given a specific set of spoofing signals within the issue space analyzed. We have generated a simple surface by fitting a combination of exponential functions to the data obtained from the GPWR Simulator. In general, trips on a low level have been observed to occur faster than those on a high level.

Universal Singular Optimal Control: Affine Systems

In this study, the problem of finding an optimal controller for nonlinear systems with one input and a reference tracking signal is approached. With the problem's formulation, any desired signal can be tracked instantly with a closed-loop controller without the need for integral terms. Presentation lies at the heart of optimal control. This study, however, does not consider the integral term, allowing tracking and stability to occur naturally. It has a broad scope with a wide range of applications, namely when dealing with affine nonlinear systems, which provide geometric control unification with asymptotic stability in some cases. A common scenario that comes from optimal control involves the minimization of integral cost functionals. Issues like asymptotic stability or even tracking to the desired reference signal have always been the main limitations. In this study, the main theorem allows the solution of optimal control problems with no-integral terms, in other words tracking problems with input/state constraints, providing closed-loop controllers. A DC motor with a pendulum in upright position is an example of an application for which singular optimal control is tested in this study. The results confirm both asymptotic stability and optimal tracking with an accuracy of 95%. The main contributions of this study include an optimal closed-loop controller with no mixed initial/final conditions, input/state constraints, asymptotic stability guarantee, a strong connection with geometric tools and finally the possibility to generalize to systems with multiple inputs. As a conclusion, general nonlinear control systems can be included in the optimal control methodology presented in this study including input/state constraints. Due to the lack of integral terms, the problem can be solved in closed form by using an optimal closed-loop controller.

Microclimate environment model construction and control strategy of enclosed laying brooder house

The growth performance and health of chicks can be significantly improved by a suitable microclimate in brooder houses. However, the microclimate of the chicken house is affected by factors such as its structure and the heat dissipation of chickens, making it is difficult to establish an accurate mathematical model and achieve effective regulation. In this paper, the environmental data acquisition system of enclosed chick brooder house was established by analyzing various environmental factors in brooder houses. According to the structural characteristics of brooder houses and the growth environment of chicks and other parameters, the microclimate simulation model of brooder houses was established using the physical law of energy balance. The coefficient of determination R2 (R-square) between simulated temperature and humidity output value and measured value was 0.7634 and 0.9740, respectively, and Root Mean Square Error (RMSE) was 1.55°C and 2.61%, respectively. The correctness of the simulation model was verified. On the basis of established microclimate models, a simulation model of fuzzy decoupling Proportion Integration Differentiation (PID) control in chicken house environments was established for the strong coupling between temperature control and general risk control system in chicken houses. Different control strategies were generated by fuzzy and logical reasoning about multiple environmental factors. The compensation coefficient was added to optimize the environmental regulation system of brooder houses. The temperature maximum deviation between the set value and the fuzzy decoupling PID controller was 0.5°C, the maximum relative error was 2.7%, the maximum deviation of relative humidity between the set value and the fuzzy decoupling PID controller was 4.93%, the maximum relative error was 10.49%.The simulation results show the control strategy meets the temperature and humidity control requirements, verify the effectiveness of the control strategy and model. The experimental results can guide the actual environmental control of brooder houses.

Parametric synthesis of control systems for the steam generator of a nuclear power plant

This paper proposes models and methods of the parametric synthesis of automatic control systems for a steam generator at a nuclear power plant, based on the modification of unconditional optimization methods. A mathematical model of steam generator control systems designed for the synthesis of model parameters has been considered. Algorithms are given for calculating level objective functions that take into consideration the limitations of variable parameters and quality indicators of problems for identifying and optimizing model parameters. The methods of unconditional optimization have been modified by introducing the operation of comparing the values of the level objective functions. Using the information technology of optimization, computational experiments were carried out to synthesize the parameters of the steam generator control systems by optimizing the level objective functions. The values of 54 parameters for the model of the control system in the PGV-1000 steam generator were identified, the value of standard deviation in the processes of 1 % from the experimental processes was achieved in the model. The result of parametric synthesis of optimal control systems for a steam generator produced a minimum control time of 131 seconds while the time of computational experiments did not exceed 10 minutes. The peculiarity of the proposed approach to the parametric synthesis of control system models is that the level objective function as a single mathematical object includes all the information about the synthesis problem. Therefore, a given approach is promising for simplifying software for solving complex problems of parametric synthesis of control systems. The considered technology of parametric synthesis of control systems could increase the degree of scientific validity of technical projects to improve various applied and promising control systems.

Synergistic balancing control for low-inertia power systems with high PV penetration: Tibet as a case study

This paper proposes a novel synergistic balancing control scheme for low-inertia power systems with high photovoltaic (PV) penetration in Tibet power grid (TPG). The mechanism and dispatching principle of choosing the suitable area control error (ACE) control mode for TPG are analyzed., An adaptive control mode switching method is proposed to meet the different control demands for the dynamically changing topologies. Then, we propose a new coordinated control scheme to dispatch hydropower generators and PV power stations. A set of optimum control objectives considering balancing control performance, regulation mileage of conventional generators, and power curtailment of PV are first presented. The control objectives are decomposed and simplified by step-by-step approximation and linearization, which are easily extended to be applied to real-world automatic generation control (AGC) systems. The simulation results show the effectiveness of the proposed solutions for synergistic balancing control by using the equivalent TPG test system. The equivalent system includes 63 generators with total power equal to 1618.5 MW and 51 PV power stations over 1100 MW. In addition, the actual operation cases present that the coordinated control between conventional and renewable generation dispatched by AGC has the basic conditions in the actual operation of the power grids, which will play a more significant role with the rapid increase of renewable generation sources in the future.

Energy Management Model of Intelligent Park Based on Improved Depth Deterministic Gradient Strategy Algorithm

The traditional distribution automation system, demand side management system, and distributed generation access and control system, respectively, solve the problems of regional distribution network power supply, customer power consumption, and new energy utilization to varying degrees. Intelligent Park evolved from the concept of intelligent park of State Grid Corporation, Modern enterprises, or residential areas that can make full use of modern communication, computer, automation, and other technologies to implement the power supply demand of the park. For the modern intelligent park with user-side temperature control load and demand response load access, an intelligent park energy management and optimal scheduling method based on deep reinforcement learning (DRL) algorithm is proposed. Through the interaction between the agent and the energy environment of the intelligent park, the control strategy is adaptively learned. This method can realize the continuous action control of energy management in intelligent park and can realize the optimal scheduling decision of intelligent park in various scenarios. Firstly, based on the characteristics and types of intelligent park aggregation unit, the environment model of intelligent park energy management system interacting with agent is established. Secondly, the basic principle of deep deterministic policy gradient deep algorithm is introduced, and on this basis, the key links of deep reinforcement learning algorithm, such as action space, state space, reward mechanism, neural network structure, and learning process, are designed. Finally, the effectiveness of the proposed algorithm is verified by an example of a city intelligent park.

Distributed adjustable robust optimal power-gas flow considering wind power uncertainty

The rapid uptake of natural gas-fired units in energy systems poses significant challenges in coordinating the electricity and gas systems. Besides, the uncertainty caused by integrated renewable energy such as wind power raises more requirements on the robustness of the operation for integrated electricity and natural gas system (IEGS). To address these challenges, this paper investigates the distributed adjustable robust optimal power and gas flow (OPGF) model for IEGS. Using linear decision rules (LDRs), we first propose an improved adjustable robust model combining with the automatic generation control systems to fully exploit its potential in dealing with renewable energy uncertainty while utilizing the controllable polyhedral uncertainty set to reduce solution conservatism. This improved LDRs based adjustable robust approach can reduce the computational burden caused by the existing decomposition based robust approach when applied to distributed optimization. Then, to preserve the information privacy and decision-making independence of subsystems, two tailored alternating direction method of multipliers (ADMM) based distributed optimization frameworks for IEGS with and without a central coordinator are presented, respectively. Effectiveness is illustrated through benchmark case studies.

Research on cooperative control strategy of multi-motor system based on fuzzy inference

General distributed multi-motor control system has a low synchronization performance and a poor coordination control accuracy. In order to improve the deficiencies, the fuzzy control method is used. Firstly, the fuzzy PID controller is designed to improve the control accuracy of a single motor in the control system; Then, aiming at the shortcomings of the traditional deviation coupling structure, an adjacent deviation coupling control strategy based on fuzzy inference and a speed compensator are designed to improve the synchronization performance of the whole control system. The simulation results show that the tracking error of single motor and the synchronization error between motors are greatly reduced.

Design of digital nuclear power small reactor once-through steam generator control system

The once-through steam generator used in the small modular reactor needs to consider the stability of the outlet steam pressure and steam superheat of the secondary circuit to achieve better operating efficiency. For this reason, this paper designs a controllable operation scheme for the steam pressure and superheat of the small reactor once-through steam generator. On this basis, designs a variable universe fuzzy controller, first, design the fuzzy control rules to make the controller adjust the PI controller parameters according to the change of the error; secondly, use the domain adjustment factor to further subdivide the input and output domain of the fuzzy controller according to the change of the error, to improve the system control performance. The simulation results show that the operation scheme proposed in this paper have better system performance than the original scheme of the small reactor system, and controller proposed in this paper have better control performance than traditional PI controller and fuzzy PI controller, what's more, the designed control system also showed better anti-disturbance performance in lifting experiment between 100% and 80% working conditions. Finally, the experimental platform formed by connecting the digital small reactor with Matlab/Simulink through OPC(OLE for Process Control) communication technology also verified the feasibility of the proposed scheme.

A Two-stage Kalman Filter for Cyber-attack Detection in Automatic Generation Control System

Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

Power-balancing Coordinated Control of Wind Power and Demand-side Response Under Post-fault Condition

As the global energy transforms to renewable-based power system, the wind power generation has experienced a rapid increase. Due to the loss of synchronous machines and its frequency control mechanisms, the gradual evolution leads to critical challenges in maintaining the frequency stability. Under post-fault condition, the wind power generation has a slow recovery due to the fault ride-through (FRT) control strategy and may cause a larger frequency deviation due to the power imbalance between the supply and demand. Then, the impacts of the frequency deviations would further cause inaccuracy and instability in the control system for wind power generation. Considering the long parking time of electric vehicles (EVs), the demand-side response is provided to support the power grid via load-to-grid technology. Thus, a power-balancing coordinated control strategy of the wind power and the demand-side response is developed. It can significantly mitigate the power imbalance, thereby resulting in the enhanced frequency stability. Finally, the simulation results are provided to validate the power-balancing coordinated control strategy.

Development of research and engineering solutions for the turbine generator and transformer digital protection and control system as exemplified by unit No. 8 of CHPP-21

Development of research and engineering solutions for the turbine generator and transformer digital protection and control system as exemplified by unit No. 8 of CHPP-21

Dynamic output feedback control for networked control systems: A sum-based discrete event-triggered approach

This paper investigates the event-based dynamic output feedback control for networked control system with actuator failures. A novel sum-based discrete event-triggered mechanism (SDETM) is proposed, whose triggering condition includes not only the current sample of system states but also previous samples. With the proposed SDETM, a dynamic output feedback controller (DOFC) is developed under a general networked control system (NCS) structure. We consider a general system structure where two network channels are involved, and the proposed SDETM is employed to both networks to extensively save network resources. A novel stability criteria is established for the closed-loop system based on the Lyapunov-Krasovskii functional method. By using the Cone-complimentarity Linearization (CCL) approach, sufficient conditions are derived to co-design the DOFC and triggering parameters. The effectiveness and advantage of the proposed method is verified by a satellite control system.

МИКРОПРОЦЕССОРНАЯ СИСТЕМА АВТОМАТИЧЕСКОГО УПРАВЛЕНИЯ СРЕДСТВАМИ ПРОТИВОПОЖАРНОЙ ЗАЩИТЫ САМОЛЕТА

Relevance The ever-increasing requirements for the safety of the use of aviation technology are inextricably linked with the problem of providing fire protection for aircraft, both military and civilian. The complexity of the problem of ensuring the fire safety of flights is associated with an increase in the intensity of the use of aviation equipment and the expansion of the range of functional tasks performed by it. The resulting complication of on-board equipment and an increase in the number of energy-intensive devices creates the prerequisites for the occurrence of fires on board the aircraft. At the same time, the remoteness of places where fires are possible, the variety of causes leading to fires, as well as the ambiguity of the conditions for the onset and spread, increase the likelihood of equipment failures, but also complicate the crew's activities. The purpose of the study is to implement a prospective aircraft fire protection system that will increase the effectiveness of existing fire extinguishing equipment. The relevance of this research project lies in the creation of an aircraft fire-fighting system that will provide timely detection of overheating/fire in the nacelles of the main power unit, in the compartments of the auxiliary power unit, baggage and cargo compartments and aircraft toilets; reliability of information from fire detection and elimination systems; increase the effectiveness of existing firefighting equipment. Aim of research The aim of the study is to develop a future aircraft fire protection system that will increase the effectiveness of existing fire extinguishing equipment. The objectives of the research project are: 1. Choosing a hardware complex for electronic indication and signaling of the aircraft fire-fighting system; 2. Integration of the fire protection complex into the general aircraft equipment control system. Research methods Analysis of modern high-performance aircraft fire protection systems and creation of a promising fire protection system based on the data obtained Results In the course of this research project, a hardware complex for electronic indication and signaling of the aircraft fire system was selected, and the integration of the fire protection complex into the control system of general aircraft equipment was carried out.

Modelling, Simulation and Sensitivity Analysis of Generator Control Systems using Coexisting and Cooperative Tools

Modelling, Simulation and Sensitivity Analysis of Generator Control Systems using Coexisting and Cooperative Tools by Javier Urquizo 1,* , Diover Bonilla 1, Francisco Rivera 1 , Rommel Chang 2 1 Escuela Superior Politécnica del Litoral, ESPOL, FIEC, Campus Gustavo Galindo Km. 30.5 Via Perimetral, Guayaquil, Ecuador2 CELEC EP Unidad de Negocio Hidronación, Central Baba, Kilómetro

Resilience Assessment and Improvement for Cyber-Physical Power Systems Under Typhoon Disasters

The cyber-physical deep coupling makes power systems face more risks under small-probability and high-risk typhoon disasters. Resilience describes the ability of cyber-physical power system (CPPS) withstanding extreme disasters and resuming normal operation. To improve the resilience assessment and analysis method of CPPS, first, a CPPS resilience assessment framework that considers the space-time metrics of disasters and the interactions of information systems and power grids is proposed, including fault scenarios extraction, response and recovery analysis, quantitative assessment of resilience. Second, from the perspective of the geographical coupling between OPGW and transmission lines and the control coupling between automatic generation control system (AGC), substation automation system (SAS) and power system, the interaction of information flow and energy flow during the failure period is analyzed. The network flow theory is used to establish an information network traffic model to describe the operating status of the information system at each stage. On this basis, a mixed integer linear programming model for DC optimal power flow considering the information network constraints and a multi-stage bi-level model for cyber-physical collaborative recovery are established. Finally, we take the IEEERTS-79 system as an example to show that the proposed method can improve the quantization accuracy comparing with the assessment method of the conventional power system, and evaluate the enhancement of typical measures at different stages.