Current State Of Plant Research Articles

ІНТЕГРАЛЬНА ІНФОРМАЦІЙНО-ОБЧИСЛЮВАЛЬНА СИСТЕМА, ЩО ЗАБЕЗПЕЧУЄ ТЕХНІЧНЕ ОБСЛУГОВУВАННЯ ЗА СТАНОМ СИЛОВИХ ГАЗОТУРБІННИХ УСТАНОВОК

The effectiveness of the chosen maintenance strategy is determined by the completeness of information about the current technical condition of the facility. The real current state of a dynamic object should specify the list and scope of preventive and remedial work for each specific maintenance object. Existing strategies and programs according to the technical condition of dynamic objects are conditionally divided into three groups: with control over the level of reliability of the operating object; with control of the parameters of the object of operation; hybrid. Power gas turbine plants are objects of operation with high functional significance. They have an insufficient degree of redundancy, as well as a low level of operational manufacturability. Therefore, it is advisable for them to apply a maintenance strategy as per the parameter control. The article proposes general principles on the basis of which an integral information-computing system for processing parameters can be built. This system is the main element of the automated control system for the technical condition of the gas turbine power plant. Synthesized adaptive system for dynamic control of parameters characterizing the current technical state of a gas turbine power plant. Closing the control loop of the technical condition of the object with negative feedback allows you to switch from the strategy of its maintenance according to the operating time to the maintenance strategy according to the state with the control of parameters. The integrated information-computing system should solve two main tasks: a reliable assessment of the current technical state of a dynamic object and prediction of a pre-failure state in the medium and long term. The main condition for organizing the maintenance of dynamic objects according to the state with the control of parameters is the possibility of continuous monitoring of the parameters characterizing the current technical state. The choice of such parameters is ambiguous, which is due to both the specific features of dynamic object and diverse nature of relationship between faults and controlled parameters. The requirements for the determining parameters are of a general nature: the parameters must have acceptable accuracy and stability of readings over time; have the greatest diagnostic value among other parameters; not depend on changes in external operating conditions and modes of operation; based on standard measurements. The system of continuous monitoring of parameters characterizing technical state of a dynamic object must be adaptive. It should change in accordance with the change in technical condition of the gas turbine power plant. The use of a set of parameters of its dynamic linear model as characteristic of the current technical state of an object is justified for a wide class of objects. The control system of such objects operates in the stabilization mode, therefore, the adaptive system of continuous monitoring synthesized on the basis of this model provides the possibility of assessing the current technical state of the gas turbine power plant and makes it possible to predict its technical state. At the same time, the adaptation of the parameters of the linear dynamic model of the object to the new technical condition of the gas turbine plant should be carried out with the least interference in the normal operation of the plant.

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN.

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.

Research on anomaly detection method of nuclear power plant operation state based on unsupervised deep generative model

In the field of traditional industrial control, anomaly detection method is mainly used to identify data items that do not match the normal operation state of the system. The traditional machine learning algorithm needs the transient operation data of normal and accident conditions to identify the abnormal state of nuclear power plant. The transient operation data of nuclear power plant during normal condition is sufficient, but it is lacks of transient operation data in accident conditions. To solve the above problems, an abnormal operation state detection method of nuclear power plant based on unsupervised deep generative model is established by using Variational Auto Encoders (VAE) and Isolation Forest (iForest) in this paper. The biggest advantage of this method is that it can only make use of the normal operation data of the nuclear power plant to make the nuclear power plant control system effectively identify whether the current state of nuclear power plant is normal operation or in accident condition. In the unsupervised deep generative model, VAE is used for data preprocessing, and iForest is used to identify abnormal operation data. Then, the method is verified in seven variable and accident conditions, such as power reduction condition, steam generator tube rupture accident and so on. The verification results show that the anomaly detection method can recognize the current abnormal condition immediately when the accident happens. And the time consumed to identify a group of operation parameters corresponding to the operation state of the nuclear power plant is about 3 ms, which can satisfy the real-time requirements of the control system. Therefore, the anomaly detection method based on unsupervised deep generative model can distinguish the normal or abnormal operation state of the nuclear power plant in real time and effectively, and provide judgment basis for accident classification and subsequent rescue.

Adaptive tracking of uncertain nonlinear systems under different types of input delays with urban traffic perimeter control application

AbstractPragmatic design method of direct model reference adaptive control (MRAC) is developed for a class of uncertain systems, where various input channels might have different delays and taking into account the effect of control saturation. To overcome the difficulty of directly predict the plant state and cope with different kinds of input delays, the proposed control design relies on implicit time‐delay prediction by using auxiliary linear Smith‐like dynamic units with adjustable gains. The design is unified to different time delay categories, that is, constant, time‐varying, or dependent on the current plant state. Within the context of the proposed approach, various adaptive control feedback strategies are designed for problems of continuous and sample‐and‐hold control implementations. Simulation results of a perimeter control for two interacting aggregate urban traffic regions are presented.

Оценка качественных и количественных характеристик состояния лесополос Новоалександровского городского округа Ставропольского края с использованием геоинформационных технологий

The article analyzes the possibility of evaluating the current state of forest protection plant in the Stavropol Territory based on remote sensing data. With the help of photogrammetric and kartometric methods, space snapshots are analyzed for the model territory of the Stavropol Territory (Novoaleksandrovsky City District). The use of geo-information technologies allowed us using a cartographic method to estimate the current state of forest belt and determine their qualitative and quantitative characteristics. In the process of digitizing and decrypting, forest belts were divided into categories by destruction. In total, 2524 forest belts with a total area of 10.95 thousand hectares were allocated during the work. As a result, it was possible to assess the qualitative state of the forest belt of the Novoaleksandrovsky urban district and reveal that entire and slightly destroyed protective forest belts are greater than in quantitative terms and in the area they occupy. Further, in the process of the study, the quantitative characteristics of the forest belt were established—the area was determined, and the length and width of all protective forest plantations of the Novoaleksandrovsky urban district was determined and entered into an attribute table. It has been established that the width of most of the protective forest belt (1281 pcs) in the area of the study is from 20 to 30 meters, and 1001 forest belts have a width of more than 30 m. The most common length of forest belt is less than 500 m. (1034 pcs.) and 500–1000 meters (680 pcs.). Square characteristics of protective forest plantations are much more inhomogeneous—so, completely scaffolding was revealed 247 pcs., With a total area of 1058 hectares. Slightly destroyed forest belt 1623 pcs. and their area are 7395 hectares. Significantly destroyed forest belt 41 pcs., with an area of 1734 hectares and fully destroyed—44 pcs., with an area of 213.8 hectares. The proposed research methodology and evaluation of protective forest plantations can be used for other territories of the Stavropol Territory for a more complete study of the state of the region of the region.

Optimal Sensor Location in Chemical Plants Using the Estimation of Distribution Algorithms

The optimal selection of sensor structures improves the knowledge of the current plant state, which is a central issue for the decision-making process. Instrumentation design is a challenging optim...

Optimal linear quadratic control for wireless sensor and actuator networks with random delays and packet dropouts

In this article, the optimal linear quadratic control problem is considered for the wireless sensor and actuator network with stochastic network-induced delays and packet dropouts. Considering the event-driven relay nodes, the optimal solution is obtained, which is a function of the current plant state and all past control signals. It is shown that the optimal control law is the same for all locations of the controller placement. Since the perfect plant state information is available at the sensor, the optimal controller should be collocated with the sensor. In addition, some issues such as the plant state noise and suboptimal solution are also discussed. The performance of the proposed scheme is investigated by an application of the load frequency control system in power grid.

Planning of alternative countermeasures for a station blackout at a boiling water reactor using multilevel flow modeling

Abstract Operators face challenges to plan alternative countermeasures when no procedure exists to address the current plant state. A model-based approach is desired to aid operators in acquiring plant resources and deriving response plans. Multilevel flow modeling (MFM) is a functional modeling methodology that can represent intentional knowledge about systems, which is essential in response planning. This article investigates the capabilities of MFM to plan alternatives. It is concluded that MFM has a knowledge capability to represent alternative means that are designed for given ends and a reasoning capability to identify alternative functions that can causally influence the goal achievement. The second capability can be applied to find originally unassociated means to achieve a goal. This is vital in a situation where all designed means have failed. A technique of procedure synthesis can be used to express identified alternatives as a series of operations. A case of station blackout occurring at the boiling water reactor is described. An MFM model of a boiling water reactor is built according to the analysis of goals and functions. The accident situations are defined by the model, and several alternative countermeasures in terms of operating procedures are generated to achieve the goal of core cooling.

Stochastic optimal linear control of wireless networked control systems with delays and packet losses

In this study, the design of the optimal decentralised state-feedback controllers is considered for a wireless sensor and actuator network (WSAN) with stochastic network-induced delays and packet losses. In particular, taking advantage of multiple controllers, the authors model the WSAN as a wireless networked control system with decentralised controllers, and then formulate the stochastic optimal state-feedback control problem as a non-cooperative linear quadratic game. The optimal control law of each controller is obtained that is a function of the current plant state and past control signals. The performance of the proposed algorithm is investigated by an unstable linearised inverted pendulum and an application of the load frequency control system in power grid.

Decentralized feedback control for wireless sensor and actuator networks with multiple controllers

In this paper, the design of the optimal decentralized full-state-feedback control is considered for a wireless sensor and actuator network with multiple controllers in the presence of stochastic network-induced delays and plant state noise. In particular, using the quadratic cost function, the design problem with decentralized controllers is formulated as a non-cooperative linear quadratic game. Then, the optimal control law of each controller is obtained that is a function of the current plant state and finite past control signals. The performance of the proposed algorithm is evaluated by a stable control system and an application of the load frequency control system in power grid.

Optimal state feedback control for wireless networked control systems with decentralised controllers

In this study, the design of the optimal decentralised full-state-feedback controllers is derived for wireless networked control systems with network-induced delays. In particular, the authors formulate the optimal decentralised control problem as a non-cooperative linear quadratic game. Then, the optimal control strategy of each controller is obtained that is based on the current plant state and the last control strategies of decentralised controllers. The proposed optimal decentralised controllers reduce to the known controller under certain conditions. Moreover, the authors illustrate the application of the proposed decentralised state feedback control to load frequency control in power grid systems.

Dynamic Maximization of Oxygen Yield in an Elevated-Pressure Air Separation Unit using Multiple Model Predictive Control

In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain optimal flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.

Optimal Online Sampling Period Assignment: Theory and Experiments

In embedded systems, the computing resources are often scarce and several control tasks may have to share the same computer. In this brief, we assume that a set of feedback controllers should be implemented on a single-CPU platform. We study the problem of optimal sampling period assignment, where the goal is to assign sampling rates to the controllers so that the overall control performance is maximized. We derive expressions relating the expected cost over a finite horizon to the sampling period, the computational delay, and the amount of noise acting on the plant. Based on this, we develop a feedback scheduler that periodically assigns new sampling periods based on estimates of the current plant states and noise intensities. Extensive experiments show that online sampling period assignment can deliver significantly better control performance than the state-of-the-art, static period assignment.

Markov reliability model research of monitoring process in digital main control room of nuclear power plant

Monitoring process is an important part in a high safety digital main control room of nuclear power plant (NPP), it is the source extracted information and found abnormal information in time. As the human factors events arisen from monitoring process recently take place more and more frequent, the authors propose a reliability Markov model to effectively decrease these abnormal events. The model mainly analyzes next monitoring object probability in terms of current information and plant state. The authors divide digital human–machine interface into two parts that are referred as logical homogeneous Markov and logical heterogeneous Markov. For the former, a series of methods of probability evaluation are proposed, such as, Markov transition probability with condition, probability distributed function with human factors, system state and alarm; for the latter, the authors propose the calculation of probability of correlation degree between last time and next time and probability calculation methods with multi-father nodes. The methods can effectively estimate the transition probability from a monitoring component to next monitoring component at time t, can effectively analyze which information is more important in next monitoring process and effectively find more useful information in time t + 1, so that the human factors events in monitoring process can greatly be decreased.

Real-time risk analysis of safety systems

Methods for plant-specific, real-time, risk assessment are presented using Bayesian analysis with copulas. These are used to develop: (i) a forecasting analyzer that predicts the frequencies of occurrence of abnormal events, (ii) a reliability analyzer that predicts the failure probabilities of safety systems involving equipment and human actions, and (iii) an accident closeness analyzer that predicts the fuzzy memberships to various critical zones, indicating the proximity of a current plant state to a failure or disaster, through the use of accident precursor data. These methods, which involve repetitive risk analysis after abnormal events occur, are especially beneficial for operations involving complex nonlinearities and multi-component interactions, helping to achieve inherently safer operations. Furthermore, these methods demonstrate the importance of using plant-specific estimates of failure probabilities rather than generic values in the risk assessment of chemical plants. In this analysis, the propagations of abnormal events through the safety systems are modeled in real-time using SIMULINK and ASPEN DYNAMICS. The analysis methods are illustrated for a continuous ethyl benzene process.

Reference Management for Control Systems Under Constraints

A supervision scheme, denoted hereafter to as a Command Governor (CG), is presented for on-line handling the reference signal to be applied to a compensated control system in order to enforce pointwise-in-time input and/or staterelated constraints. A CG is basically a memoryless nonlinear device which, on the base of current plant state and reference signal, produces its action by modifying the reference signal in order to avoid potential constraint violations. An application to a bank-to-turn autopilot design is presented where the CG is used for governing the demanded pitch acceleration in order to avoid violation of physical constraints on the elevator angle.

Effective Use of Sensor Readings in On-Line Plant Monitoring and Its Application to Boiling Water Reactor Power Distribution Calculations

Effective use of the measured readings of sensors in on-line plant monitoring has been studied, based on an error theory. Both the measured reading and the calculated one, obtained by an analytical model of the plant, are treated as observed values, and the maximum-likelihood estimator is determined so as to minimize its mean-squared error. The difference between the estimator and the calculated reading is used to adapt the model to the current plant state and to increase accuracy of the calculated reading. The index of systematics, which indicates the mutual independence of the two observed values, has been evaluated to determine the step in the procedure where the above adaptation is to be inserted. The error-theory-based model adaptation procedure has been experimentally applied to boiling water reactor power distribution calculations, and its performance has been verified in simulation calculations at different core states and different numbers of in-core neutron monitors by evaluating the expected error of the calculated readings. Compared to the adaptation, which uses the measured readings instead of the estimator, the error is typically lowered by more than 2% and is less affected by the number of monitors.