Supervisory Control Method Research Articles

The research on Adaptive Fuzzy Tracking Supervisory Control in the control system of average coolant temperature of lead–bismuth-cooled reactor under multiple operating conditions in the marine environments

Lead–bismuth-cooled reactor nuclear power equipment is severely affected by marine environments, causing operational attitude changes such as heeling and rolling. Conventional controllers cannot track set points rapidly. Therefore, this paper proposes an Adaptive Fuzzy Tracking Supervisory Control method. Firstly, the reactor mathematical model based on fuzzy basis functions is obtained through fuzzy mathematics. Secondly, a coolant average temperature tracking controller is designed on Lyapunov stability theory. To ensure the system returns to stable domain, supervisory compensatory control algorithm is developed. Next, the parameters of fuzzy model are adjusted using Fuzzy universal approximation theorem. By introducing discrepancies between actual system and fuzzy model outputs, parameter adaptive law is designed through Lyapunov theorem. This enables real-time parameter adjustment for fuzzy model and fuzzy tracking supervisory controller, enhancing load tracking performance of coolant’s average temperature. Finally, simulation experiments demonstrate that adaptive fuzzy tracking supervisory controller has stronger adaptability to multiple operating conditions under marine environments.

Neural network-based distributed intelligent supervisory control for multi-agent systems with unknown input powers

In this paper, a synchronisation problem is studied for a class of nonlinear leader-following systems. Based on adaptive control with some algebra lemmas, we propose a distributed control scheme. This scheme ensures each follower to asymptotically synchronise with the leader. Compared with the existing works where system input powers are assumed one, the input powers considered in this paper are unknown but larger than one. Based on Graph theory, Lyapunov theory and radial basis function (RBF) networks, we design a distributed adaptive supervisory control method. It is proven that the consensus among the leader and followers are achieved and all the signals including tracking errors asymptotically converge to a small neighbourhood of the origin. Finally, simulation results are displayed to demonstrate the effectiveness of control scheme.

Modelling and Formal Verification Approach for Microgrid Energy Management Systems under Random Load

This paper presents the modelling and a formal verification approach of energy management control systems for microgrids that are subject to unknown demand and uncertainties in the availability of renewable energy sources. The main objective of this study is to develop a management system for a microgrid composed of renewable and non-renewable energy sources, specifying the different possible modes. A supervisory control method is proposed to autonomously control and monitor a microgrid and maintain more stable energy flows with maximum utilization of available resources. A timed automata network model of the microgrid has been developed by modeling the dynamics of components such as PV, BESS, diesel generator, load, and controller. Possible operating modes in the microgrid between sources and load are derived and classified into allowed and unallowed operating modes. Verification of the designed microgrid models was performed using simulation and formal methods. The advantageous performance of the proposed strategy is illustrated via a numerical example.

Adjacent initial states-based differential privacy for probabilistic labeled Petri nets

Privacy protection has received widespread attention from the community of discrete event systems to protect the sensitive information of users or organizations from being leaked. The existing privacy protection methods cannot protect the state information of probabilistic discrete event systems via repeated observations, which represents the information pertaining to system resource configurations. This work introduces differential privacy into the framework of probabilistic labeled Petri nets to solve the problems pertaining to the initial state protection. For two initial states that are adjacent under a specified measure, a state differential privacy verification method is proposed by determining whether the probability distributions of observations generated from adjacent initial states are similar. An external attacker is unlikely to infer the initial state via repeated observations if the system satisfies state differential privacy for certain adjacent initial states. For a probabilistic labeled Petri net, which does not satisfy state differential privacy, a supervisory control method is proposed for enforcement. A maximally permissive controller can be constructed based on the control specification proposed in this paper. Experimental studies show that the method proposed in the paper can effectively protect the privacy of given adjacent initial states.

Sizing Optimization Algorithm for Vehicle-to-Grid System Considering Cost and Reliability Based on Rule-Based Scheme

The most widely two Renewable Energy Sources (RESs) used are solar and wind as naturally found sources due to the provided merits as clean, free of charge, and environmentally friendly. However, they are facing limitations in intermittency. This article aims to utilize natural resources integrated with the utility grid and Electric Vehicle (EV) to provide a hybrid system with a minimum of two objectives namely Cost of Electricity (COE) and reliability using the Losses Power Supply Probability (LPSP) method. The two mentioned objectives are considered to satisfy the residential load demand with EV in terms of Vehicle-to-Grid (V2G) as this article considered. The mentioned objective has been addressed by the Improved Antlion Optimization Algorithm (IALO) and coupled with a high supervisory control method called Rule-Based Energy Management Strategy (RB-EMS) to guarantee to spread the power among the system component. Optimization results show the hybrid integration of the utilized RESs with the Battery (BT) gives the most economic and reliable system for the study area integrated with the EV battery. The gained result was validated with a natural-inspired metaheuristic algorithm called Particle Swarm Optimization (PSO). This article assesses the effect on the RESs generators to achieve an economic and reliable system.

Gaussian Process Approximate Dynamic Programming for Energy-Optimal Supervisory Control of Parallel Hybrid Electric Vehicles

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively operated to meet the required instantaneous traction power for the desired vehicle speed while satisfying their physical limits. Because the efficiencies of the engine and motor vary with different operating speeds and torques, the main issue of energy-efficient power management is to allocate the power demand among the power sources by achieving maximum power conversion efficiencies and satisfy the operating limits. For an efficient power allocation, an optimal control problem is formulated, and a global solution is found through deterministic dynamic programming (DP). Owing to the curse of dimensionality and uncertainties in real driving, DP solutions are not directly applicable in real time. To resolve the limitations of DP, we employ a non-parametric Bayesian function approximation technique using a Gaussian process (GP). The offline DP solutions obtained from a set of real vehicle driving test data were used to learn a state-dependent probabilistic value function through Gaussian process regression. For online implementations, a receding horizon control scheme was applied for the feedback control of the power management. In comparison with the existing charge sustaining strategy and charge depleting and charge sustaining mixed controllers, we recorded fuel efficiency improvements of over 4.8% and 7.3%, respectively, in a mixed urban-suburban route.

Islanded AC/DC microgrids supervisory control: A novel stochastic optimization approach

Load demand has locally met the distributed energy resources (DERs) under an infrastructure that is called microgrid. In this paper, a new supervisory control approach is presented based on MPC method, using a stochastic optimization model. The objective function (OF) of the proposed central controller consists of the combined cost-based and system-based parts. Indeed, the setpoints of DERs are updated by the supervisory control method to regulate voltage and frequency to their nominal values as well as to decrease the generation costs. The stochastic optimization formulation is conducted based on MPC structure. Moreover, the uncertain nature of DERs is considered in the optimization formulation and MPC framework using several scenarios. In addition, the effect of reactive power on frequency is considered in this paper and the results show better voltage and frequency regulations. The algorithm is implemented on a modified IEEE 33-bus test system with AC and DC parts. Furthermore, several cases and the sensitivity analysis are accomplished to show the efficiency of the proposed model.

Design of decision-making support system in power grid dispatch control based on the forecasting of energy consumption

Electric grids are constantly expanding, and supervisory control and management methods must also be improved and changed in order to maintain reliable and safe power supply to consumers. This article proposes a methodology for supporting the adoption of dispatch decisions on the base electrical load forecasting. The energy consumption forecast is based on a deep neural network, and depending on the value obtained, a recommendation for optimising the operation of the energy system is proposed. Thus, dispatch service employees will be able to make decisions on managing the energy system based on the recommendations received, which will increase the speed of decision-making and improve the efficiency of the entire dispatch centre. Also, intelligent data processing and the proposed decisions allow us to consider and compare the factors that may be missed because of the human factor when the information is processed directly by the dispatcher. The use of retrospective data about the consumed power, the ambient temperature, and the type of day of the week is proposed as a knowledge base for forecasting energy consumption and training a neural network. The proposed neural network made it possible to achieve a value of the average absolute error of MAPE prediction of 1.922%. The obtained accuracy allows the use of forecasting results for dispatch control.

Structural Design of Supreme Controller with Uncontrollable Transitions

In the framework of Petri net (PN), the structural supervisory control of discrete event system (DES) is an exciting method to design controller in the presence of uncontrollable transitions. Especially, this method addresses the controllability problem existing in the desired functioning PN. The controllability condition defined by uncontrollable synchronization can be expressed as constraints (GMEC). This leads to design place invariant-based controller, implemented through control places connected to plant transitions. The controller is maximally permissive if the transitions are controllable. Implying that the controller insures that the constraints are never violated directly or may be violated through the firing of uncontrollable transitions. But, if one control place is connected to uncontrollable transition, then the controller is non-admissible, since it cannot prevent such transition. The common idea is to transform the constraints or to displace the controller arcs. Unfortunately, the constraints transformation is computationally complex and not structural, while the arcs displacement approach is unsystematic. Our idea consists to iterate the structural supervisory control method to ensure a systematic displacement of controller arcs, so that no control place is input place of uncontrollable transition. This approach focuses on structural design of a less restrictive and admissible controller, namely the supreme controller

A Supervisory Control Method for Neutron Scattering Superconducting Magnet

A supervisory control for neutron scattering superconducting magnet is proposed in this paper, which adopts hierarchical control strategy. In the field layer, programmable logic controller (PLC) based on monitoring control and data acquisition system is used for automatic control of various working conditions. PLC and upper monitoring computer are independent of each other in the equipment control layer, and independent operation is not dependent on the upper computer, which ensures the independence and safety of the process. The running parameters of superconducting magnet can be detected in real-time with the coverage of local area network (LAN), and the significant equipment of neutron scattering superconducting magnet can be controlled by using real-time control method in the mobile environment. In case of abnormal situation, the backfeed control will be used to realize remote alarm and other functions, which ensures the normal running of neutron scattering superconducting magnet safely and reliably.

Approaches to deep learning based manipulating strategy reconstructions for complex chemical processes

Modern complex chemical process operations produce a large amount of process monitoring and control time series data, which are potentially useful to extract valuable operating experiences and manipulating rules to improve the intelligence of process operations. Previous researches show that time series clustering is an effective method to mine historical control sequences. However, the actual working conditions often deviate from the historical data, making it difficult to reconstruct accurate process regulation manipulating strategies. In response to this problem, this paper proposes a manipulation strategies reconstruction method using a convolutional neural network model (MSR-CNN). Therein, the time series are hierarchically clustered according to the Levenshtein distance to obtain different classes of process disturbance states, and the corresponding manipulated sequences fragments obtained are treated with a symbolic aggregation approximation. Then the improved convolutional neural network is used for deep learning and manipulation strategies reconstruction of the process disturbance states and the corresponding manipulating sequences strings. Finally, applying to a numerical example and an ethanol–water distillation tower unit, the proposed method is proved to be effective. Compared with traditional supervisory control methods, our contributions can help well construct complex chemical process control strategies less dependent on human operators’ operating experiences.

Controlled adjustments of indoor microclimate parameters for building’s energy demand management

Indoor air quality and thermal comfort are regulated by heating, ventilation, and air-conditioning (HVAC) system modules, which play an increasing role in integrating buildings into future energy networks as active nodes permitting flexible consumption management. A building’s HVAC devices are controlled by automation and control systems consisting of basic and supervisory control layers. Local discrete-mode controllers of HVAC system devices, which receive control signals proposed by a supervisory control method, cause a performance gap between these control layers. Here, we study the performance gap of discrete-mode controllers and examine how such controllers can maintain set-points, using simulation studies developed in the TRNSYS software environment. Considering a set of different devices operating within one room to manage its indoor air temperature and quality, we show that coordination of their discrete-mode controllers may yield a sizeable, larger than 10%, reduction of the heat use and average thermal load. However, such coordination may also entail indoor comfort violation, meaning that there should be a trade-off between comfort and energy performance. We propose controlled adjustments of indoor microclimate parameters to manage heat use (kWh) and thermal load (kW) within demand-side management programs. According to our simulation studies for discrete-mode controllers, the heat use and average thermal load may be reduced by up to 14.1%, ceding the indoor temperature set-point profile by 1 °C and the proportion of fresh air in the room by 50%. Hence, the controlled adjustments of various indoor microclimate parameters would be an excellent approach for integrating HVAC system field devices with discrete-mode controllers, and thereby buildings, into future energy networks providing them extra flexibility.

A Method for Supervisory Control of Manipulator of Underwater Vehicle

A novel method for supervisory control of multilink manipulators mounted on underwater vehicles is considered. This method is designed to significantly increase the level of automation of manipulative operations, by the building of motion trajectories for a manipulator working tool along the surfaces of work objects on the basis of target indications given by the operator. This is achieved as follows: The operator targets the camera (with changeable spatial orientation of optical axis) mounted on the vehicle at the work object, and uses it to set one or more working point on the selected object. The geometric shape of the object in the work area is determined using clouds of points obtained from the technical vision system. Depending on the manipulative task set, the spatial motion trajectories and the orientation of the manipulator working tool are automatically set using the spatial coordinates of these points lying on the work object surfaces. The designed method was implemented in the C++ programming language. A graphical interface has also been created that provides rapid testing of the accuracy of overlaying the planned trajectories on the mathematically described surface of a work object. Supervisory control of an underwater manipulator was successfully simulated in the V-REP environment.

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

Control Strategies for Bidirectional DC-DC Converters: An Overview

The energy produced by renewable energy sources has variations as these sources are sensitive to weather conditions. To overcome this problem, storage devices like batteries are used for stabilization. Bidirectional DC-DC converter is the main component to interface renewable energy sources with energy storage devices. With the help of proper switching techniques, these converters have the ability to increase or decrease the level of the voltages and can handle the power flow in both directions, from source to storage device (forward direction/charging mode), as well as from storage device to source (backward direction/discharging mode). Hence for an efficient system, the control of power flow must be very effective. This paper presents an overview of various bidirectional DC-DC converter’s classical and supervisory control methods that have been proposed in recent years.

Method of Supervisory Control of Manipulator Mounted on Underwater Vehicle

The report describes the new supervisory control method for a multi-link manipulator mounted on an underwater vehicle, providing visual control of manipulation operations. The proposed method allows to build complex spatial movement trajectories of a multi-link manipulator working tool along a work object surface, using data obtained from onboard multibeam sonars or technical vision systems. Target designations of the operator determine the trajectory type, its position and orientation in a work area. To form target designations and visual observation of manipulator working tool movements along a given trajectory, a TV camera is used that changes its optical axis spatial orientation. The developed method is implemented in the C++ programming language, as well as numerical simulation of a multi-link underwater manipulator supervisory control in the virtual V-REP environment. To visualize the method, the graphical shell is written using "OpenGL Core".

Discussion on “Novel Supervisory Control Method for Islanded Droop-Based AC/DC Microgrids”

The discussers state that the authors of the original work [ibid., vol. 34, no. 3, pp. 2140-2151, May 2019] are to be commended for the significant contribution in the area of control of islanded microgrids. The work presents a novel supervisory control method for an islanded hybrid AC/DC microgrid (HMG) with a high renewable energy source (RES) penetration. The proposed control method adjusts the set-points of the distributed generators (DGs), battery energy storage systems (BESSs), RESs, and interlinking converter (IC) optimally to restore the frequency and voltage to nominal values while reducing the generation cost at short time intervals. The authors' comments on several points are questioned.

Closure to Discussion on “Novel Supervisory Control Method for Islanded Droop-Based AC/DC Microgrids”

Closure to Discussion on “Novel Supervisory Control Method for Islanded Droop-Based AC/DC Microgrids”

Distributed Supervisory Secondary Control for a DC Microgrid

This article investigates the current sharing and voltage regulation problem of a DC microgrid by a distributed supervisory control method. In the existing secondary control methods, current sharing feedback control loop and voltage regulation feedback control loop are linearly integrated to shift the droop equation. However, the linear combination of two loops may easily offset the control effect of each other, as the control dimension is less than the objective dimension. This fact motivates us to design a distributed supervisory secondary control (DSSC) scheme to achieve a more flexible configuring of each converter. The DSSC consists of two candidate controllers and a supervisor. The two controllers refer to current sharing configuring and DC bus voltage regulation respectively, and the supervisor is to orchestrate the switching between candidate controllers based on the system environment of DC microgrid. Under the proposed DSSC, each converter selects to configure the current sharing or regulates the DC bus voltage according to its need. Theoretical stability analysis is rigorously conducted. Finally, both simulation and experiment results are presented to demonstrate the effectiveness of DSSC.

Study on the application of reinforcement learning in the operation optimization of HVAC system

Supervisory control can be used to optimize the HVAC system operation and achieve building energy conservation, while reinforcement learning (RL) is considered as a promising model-free supervisory control method. In this paper, we apply RL algorithm to the operation optimization of air-conditioning (AC) system and propose an innovative RL-based model-free control strategy combining rule-based and RL-based control algorithm as well as complete application process. We use a variable air volume (VAV) air-conditioning system for a single-storey office building as a case study to validate the optimization performance of the RL-based controller. We select control strategies with the rule-based control controller (RBC) and proportional-integral-derivative (PID) controller respectively as the reference cases. The results show that, for the air supply of single zone, the RL controller performs the best in terms of both non-comfortable time and energy costs of AC system after one-year exploration learning. The total energy consumption of AC system reduced by 7.7% and 4.7%, respectively compared with RBC and PID strategies. For the air supply of multi-zone, the performance of RL controller begins to outperform the reference strategies after two-year exploration learning and two-year buffer stage. From the seventh year on, RL controller performs much better in terms of both non-comfortable time and operating costs of AC system, while the operating cost of AC system is reduced by 2.7% to 4.6% compared with the reference strategies. In addition, RL controller is more suitable for small-scale operation optimization problems.