•PMS for hybrid storage-based grid-connected microgrid.•Application of model predictive control in PMS.•Real-time simulation of dynamic PMS of the microgrid.•The smooth operation of the Fuel cell in ...the microgrid.
In this study, an efficient and reliable dynamic power management system (PMS) is proposed for microgrids (μGs) based on hybrid energy storage systems. Owing tothe differences in the response times of the different components (i.e., the battery, supercapacitor, and fuel cell) of the μG, efficiently allocating the power between the different devices is a challenging task for system designers. To address this issue and optimize the operation of the μG, a dynamic PMS is necessary. The contribution of this study is the application of model predictive control (MPC) for operating and controlling such μGs. The MPC method is used to manage power electronic devices such as DC-DC converters (for the battery, fuel cell, and supercapacitor) and grid-connected inverters. After the proposed PMS is successfully implemented in simulations, itsreal-time performance is validatedunder different operating scenarios that could occur in actual situations. The proposed PMS is found to be effective in controlling the control and operation of the μG.It can regulate the DC bus voltage during transient power imbalances, control the fuel cell current slope as per the requirement, and smoothen the operation between isolated and grid-connected modes.
Developing an effective and reliable power flow (PF) tool is of great significance for the steady-state analysis of autonomous AC/DC hybrid microgrids (MGs). In this paper, a generalized PF model is ...constructed based on holomorphic embedding (HE). With a well-designed embedding technique, the nonlinear PF problem is modeled into an embedded system, which comprehensively accounts for the distinct features of islanded hybrid MGs (such as droop-regulated distributed generations (DGs) and AC/DC interlinking converters (ICs), the variation of system frequency and its effect on admittance matrix and loads in AC part). The embedded system has a generalized structure with a constant sparse matrix, which not only enables it to be solved recursively and efficiently, but also facilitates wide applicability and convenient operation. Moreover, the proposed HE-based model features a deterministic germ that relates to a physical state, which allows it to inherit the merit of the canonical embedding in converging to upper-branch solution reliably and unambiguously, without dependency on initial estimates of state variables. The feasibility and applicability of the proposed model are validated on 3 test systems of different sizes. Furthermore, the computational efficiency and convergence performance are also evaluated.
•Holomorphic embedding power flow modeling of islanded AC/DC hybrid microgrids.•Compatibility with droop regulation and distinct features of hybrid MGs.•Deriving reliable power flow solutions without dependency on initial guesses.•Generalized framework for wide applicability and efficient operation.•Experimental validation has been done on hybrid MGs of different sizes.
A novel unified control of the dc-ac interlinking converters (ICs) for autonomous operation of hybrid ac/dc microgrids (MGs) has been proposed in this paper. When the slack terminals in the ac and dc ...MGs are available, the ICs will operate in autonomous control of interlinking power between the ac and dc subgrids, with the total load demand proportionally shared among the existing ac and dc slack terminals. With a flexible control variable added in power control loop, design of the interlinking power control, and droop features of ac and dc MGs can be decoupled. Moreover, this control variable can be tuned flexibly according to different power control objectives, such as proportional power sharing in terms of capacity (which is considered in this paper), interlinking power dispatch, and other optimal power dispatch algorithms, ensuring a well-designed flexibility and compatibility. Furthermore, if the dc MG or the ac MG loses dc voltage control or ac voltage and frequency control capability due to failures of operation of its slack terminals, the ICs can automatically and seamlessly transfer to dc MG support or ac MG support control modes without operation mode detection, communication, control scheme switching, and control saturation. In order to enhance the stability of the proposed unified control in different modes with different control plants, a phase compensation transfer function has been added in the power control loop. After thorough theoretical analysis and discussions, detailed simulation verifications based on PSCAD/EMTDC and experimental results based on a hardware experimental MG platform have been presented.
•Practical systematic method for safe, fast and explainable online RL implementation without need for simulation model.•Incorporates domain knowledge in RL problem formulation.•Two process control ...applications described using DDPG RL algorithm.•Demonstrates capability to quickly learn while avoiding unsafe operations.•Potential for intelligent autonomous operations in process industry.
Industrial process control using model-based technologies is well established. These technologies are typically non-adaptive and so have limitations. Reinforcement Learning (RL) provides a model-free adaptive alternative. RL is a type of machine learning (ML) where models or data sets of the environment are not necessary before learning can start. It generates data, by exploring the environment and then learn the behavior from it. Though RL has been successfully applied for learning and playing various games such as Go, Chess, Atari; its application to continuous process control problems is not trivial. There is a need for online RL implementation to be safe, fast learning and explainable when applied to industrial control problems. Rather than adding to the extensive research on augmenting existing RL algorithms, the paper presents a unique systematic method of formulating the RL problem incorporating domain-specific knowledge about process constraints and objectives, resulting in reduced dimensionality, along with modifications to the exploration process, applicable to any model free RL algorithm supporting continuous states and actions, to enhance safety, speed and explainability of online RL implementation without requiring a simulation model. The approach is successfully implemented on two multivariable processes: a simulated distillation column and a temperature control lab setup using the Deep Deterministic Policy Gradient (DDPG) algorithm. The work demonstrates that the presented method is applicable to multivariable, noisy, non-linear processes with disturbances. It will further the potential of introducing the advances in Artificial Intelligence and Machine Learning algorithms for intelligent process control capable of enabling autonomous operation in the process industry.
•We consider a semi-autonomous transit system within a geofenced area.•Bus platooning is introduced to ensure dynamic bus capacity.•Bus driver constraint is modeled endogenously.•Bus dispatch time, ...platoon size, and service type are jointly optimized.•The proposed model can reduce both operating cost and passenger waiting time.
Autonomous driving holds great promise for easing the driver shortage issue that has occurred in many countries. In this paper, we consider a semi-autonomous transit system where (1) individual buses could form platoons to increase bus capacity during peak hours and operate separately during off-peak hours; and (2) buses may drive autonomously in certain geofenced areas yet need to be guided by human drivers elsewhere (e.g., SAE level 4, semi-autonomous). We model buses driving autonomously within the geofenced area as short-turn bus service, and jointly optimize bus dispatch headway, platoon size and service type through an integer programming model to address the driver shortage issue. The proposed model considers real-world and micro-level bus operation processes for mixed traffic of short-turn and full route buses, with bus fleet and driver workforce constraints being modeled endogenously. Since the model is non-linear, we further develop a radial basis function-based surrogate framework to solve the model efficiently. Experimental results show that, compared to the conventional bus service that uses fixed bus capacity only, semi-autonomous bus platooning service reduces operation cost significantly while reducing passenger wait time. System performance variations of the proposed model under different driver availabilities are also examined.
Visual servoing can greatly improve underwater robot manipulation accuracy and automation. Its main purpose is to control the end-effector pose of underwater vehicle manipulator systems (UVMS) ...relative to the target by using features extracted from image. However, underwater manipulation visual servoing suffers from the drawback of perception limitation of visibility range, obscure and low contrast image, inaccurate camera calibration and model parameters. This article has reviewed the state of major visual servoing method on UVMS including position based visual servoing (PBVS) and image based visual servoing (IBVS). On the problem of underwater manipulation and vision control, a novel hybrid strategy of visual servoing control based on uncalibrated visual servoing has been issued. Simulations and comparisons on PBVS, IBVS and proposed hybrid strategy have been made. The article is concluded with challenge analysis and future research trends discussions.
•The major visual servoing method on UVMS including PBVS and IBVS have been systematically analyzed in detail. .•A novel hybrid control strategy based on uncalibrated visual servoing has been issued, on the problem of underwater manipulation and vision control.•Simulations are performed to verify the proposed method, which has contributed to the realization of high precision operations of UVMS for long distance.
Advanced reactors to be deployed in the coming decades will face deregulated energy markets, and may adopt flexible operation to boost profitability. To aid in the transition from baseload to ...flexible operation paradigm, autonomous operation is sought. This work focuses on the control aspect of autonomous operation. Specifically, a hierarchical control system is designed to support constraint enforcement during routine operational transients. Within the system, data-driven modeling, physics-based state observation, and classical control algorithms are integrated to provide an adaptable and robust solution. A 320MW Fluoride-cooled High-temperature Pebble-bed Reactor is the design basis for demonstrating the proposed control system.
The hierarchical control system consists of a supervisory layer and low-level layer. The supervisory layer receives requests to change the system’s operating conditions (e.g., the current reactor power to meet a load-follow), and accepts or rejects them based on constraints that have been assigned. Constraints are issued to keep the plant within an optimal operating region. The low-level layer interfaces with the actuators of the system to fulfill requested changes, while maintaining tracking and regulation duties. To accept requests at the supervisory layer, the Reference Governor algorithm was adopted. To model the dynamics of the reactor, a system identification algorithm, Dynamic Mode Decomposition, was utilized. To estimate the evolution of process variables that cannot be directly measured (e.g., the propagation of delayed neutron precursors), the Unscented Kalman Filter, incorporating a nonlinear model of nuclear dynamics, was adopted. The composition of these algorithms led to a numerical demonstration of constraint enforcement during a 40% power drop transient (at a rate of 5%/min). Uncontrolled secondary-side temperatures were successfully constrained. Adaptability of the proposed system was demonstrated by modifying the constraint values, and enforcing them during the transient. Robustness was also demonstrated by enforcing constraints under noisy environments.
•Designed a supervisory control system to enable Autonomous Operation of NPPs.•Interfaced control system with state-of-the-art system-code System Analysis Module.•Demonstrated constraint enforcement during power transients for the FHR design.•Demonstrated robustness of the framework under noisy output measurements.
This paper presents a fuzzy logic controller (FLC) for autonomous (islanded) operation of an electronically interfaced distributed generation unit and its load. In the grid-connected mode, the ...voltage-sourced converter is operated in the active and reactive power (PQ) control mode, where a conventional control scheme is used to control the active and reactive power exchange with the grid. In the islanded mode, the proposed FLC is used to control the voltage of the islanded system despite the load variability and uncertainties. In addition, this paper also presents the use of the black-box nonlinear optimization technique to tune the parameters of the membership functions of the FLC in order to achieve optimal performance. The salient features of the proposed FLC are: 1) efficient to deal with the nonlinear systems; 2) design does not depend on the mathematical model of the system; and 3) less sensitive to the parameters variation than the conventional controllers. The frequency of the islanded system is dictated through the use of an internal oscillator. The effectiveness of the proposed FLC in controlling the voltage of the islanded system, irrespective of the load variability, is extensively validated based on simulation studies in the PSCAD/EMTDC environment. Moreover, the paper highlights the superiority of the proposed FLC over the conventional proportional-integral controllers through comparing the transient responses of the system based on both controllers.
•Autonomous control algorithm for safety functions was modeled with a FHF and an LSTM.•LSTM network was trained using a simulator and validated to demonstrate the effectiveness of the ...algorithm.•Autonomous control could manage the plant safety better than the current automation plus human control.
With the improvement of computer performance and the emergence of cutting-edge artificial intelligence (AI) algorithms, an autonomous operation based on AI is being applied to many industries. An autonomous algorithm is a higher-level concept than conventional automatic operation in nuclear power plants (NPPs). In order to achieve autonomous operation, the autonomous algorithm needs to include superior functions to monitor, control and diagnose automated subsystems. This study suggests an autonomous operation algorithm for NPP safety systems using a function-based hierarchical framework (FHF) and a long short-term memory (LSTM). The FHF hierarchically models the safety goals, functions, systems, and components in the NPP. Then, the hierarchical structure is transformed into an LSTM network that is an evolutionary version of a recurrent neural network. This approach is applied to a reference NPP, a Westinghouse 930 MWe, three-loop pressurized water reactor. This LSTM network has been trained and validated using a compact nuclear simulator.
