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An architecture for a control system in a network, and devices and techniques for implementing the same. The architecture may comprise a logic controller isolated from the network by an interface controller and a bridge interface between the interface controller and the logic controller.
Based on a class of industry processes, a distributed fault diagnosis and a collaborative operational fault tolerant control algorithms are presented for an irreversible interconnected stochastic distribution control system with boundary conditions. Different from the existed collaborative fault tolerant controllers, the controller does not need to change after fault occurs, the system tracking performance can be maintained by adjusting the setpoint of the whole system. Simulations are also included to show the effectiveness of the proposed algorithm and encouraging results have been obtained.
Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. Here, in order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.
To release the 'hidden inertia' of variable-speed wind turbines for temporary frequency support, a method of torque-limit-based inertial control is proposed in this paper. This method aims to improve the frequency support capability considering the maximum torque restriction of a permanent magnet synchronous generator. The advantages of the proposed method are improved frequency nadir (FN) in the event of an under-frequency disturbance; and avoidance of over-deceleration and a second frequency dip during the inertial response. The system frequency response is different, with different slope values in the power-speed plane when the inertial response is performed. The proposed method is evaluated in a modified three-machine, nine-bus system. The simulation results show that there is a trade-off between the recovery time and FN, such that a gradual slope tends to improve the FN and restrict the rate of change of frequency aggressively while causing an extension of the recovery time. These results provide insight into how to properly design such kinds of inertial control strategies for practical applications.
Inverter-based voltage regulation is gaining importance to alleviate emerging reliability and power-quality concerns related to distribution systems with high penetration of photovoltaic (PV) systems. This paper seeks contribution in the domain of reactive power compensation by establishing stability of local Volt/VAr controllers. In lieu of the approximate linear surrogate used in the existing work, the paper establishes existence and uniqueness of an equilibrium point using nonlinear AC power flow model. Key to this end is to consider a nonlinear dynamical system with non-incremental local Volt/VAr control, cast the Volt/VAr dynamics as a game, and leverage the fixed-point theorem as well as pertinent contraction mapping argument. Numerical examples are provided to complement the analytical results.
This paper presents an optimal preventive control (OPC) method to improve the power system transient stability via tuning the transient parameter of virtual synchronous generators. The novelty of this work is that we formulate the preventive control as an optimization problem so that inverter parameters can be adjusted at the pre-contingency stage. A reinforcement learning (RL)-driven method is proposed to solve the OPC problem with the fault energy-based reward function. An ANDES-based RL environment is also developed. Versatile functions included in the proposed environment have been presented in this paper. The proposed OPC formulation, the RLdriven method, and the fault energy-based reward function are verified on several standard test systems.
The project addresses both fundamental and practical challenges of GFM/GFL inverter control for the power grids with high inverter based resources (IBRs) penetration. A data- driven modeling technique is applied to accurately model dynamics of PWM inverters, including electromagnetic-transient (EMT). Systematic and integrative designs of grid- forming (GFM) and grid-following (GFL) primary controls are developed to guarantee system performance under either normal or abnormal operating conditions without violating constraints. This modeling and control framework provides black-start capability in case of an outage without relying on rotating generators, and its secondary control is also shown to enhance resilience against cyber-physical attacks.
The majority of energy use in controlled environment agriculture is typically from electric lighting. Certain lighting controls can help minimize the energy required in these operations. This analysis simulates a greenhouse with different types of lighting controls in different U.S. climates to explore the effects of the lighting controls on energy use.
When operated, the climate control system is the largest auxiliary load on a vehicle. This load has significant impact on fuel economy for conventional and hybrid vehicles, and it drastically reduces the driving range of all electric vehicles (EVs). Heating is even more detrimental to EV range than cooling because no engine waste heat is available. Reducing the thermal loads on the heating, ventilating, and air conditioning system will extend driving range and increase the market penetration of EVs. Researchers at the National Renewable Energy Laboratory have evaluated strategies for vehicle climate control load reduction with special attention toward grid connected electric vehicles. Outdoor vehicle thermal testing and computational modeling were used to assess potential strategies for improved thermal management and to evaluate the effectiveness of thermal load reduction technologies. A human physiology model was also used to evaluate the impact on occupant thermal comfort. Experimental evaluations of zonal heating strategies demonstrated a 5.5% to 28.5% reduction in cabin heating energy over a 20-minute warm-up. Vehicle simulations over various drive cycles show a 6.9% to 18.7% improvement in EV range over baseline heating using the most promising zonal heating strategy investigated. A national-level analysis was conducted to determine the overall national impact. If all vehicles used the best zonal strategy, the range would be improved by 7.1% over the baseline heating range. This is a 33% reduction in the range penalty for heating.
This paper summarizes an updated process for risk-informed surveillance frequency control program (SFCP) implementation at the Catawba Nuclear Power Plant in the past year. This program includes selection and prioritization of specific target surveillance test interval extensions; and development, review, and implementation of surveillance test risk informed documented evaluation (STRIDE) packages designed to support extension of conventional surveillance requirement test intervals, in accordance with 'Risk-Informed Technical Specifications Initiative 5b, Risk-Informed Method for Control of Surveillance Frequencies, Industry Guideline,' NEI 04-10, Revision 1 . The scope of work associated with STRIDE development includes probabilistic risk assessment (PRA) case studies, deterministic assessment (DA) evaluations, and, where required, instrument drift evaluation (IDE). The STRIDE implementation efforts have also included support of independent decision-making panel (IDP) meetings at the implementing power stations and IDP member training. The purpose of this paper is to provide a presentation of a refined process for STRIDE development with a focus on instrumentation and control systems based on author experience, which includes support for the development of multiple STRIDE packages for the Catawba Nuclear Plant. This paper outlines a framework for practical implementation of an SFCP within the context of an integrated risk-informed performance-based regulation application program with emphasis on instrumentation and control systems. (authors)
Optimal control system theory applied to manual control systems - adaptive control - mathematical models
This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses.
To enable a self-regulating capability for heat pipe (HP) microreactors, an anticipatory control strategy achieved via model predictive control (MPC) could proactively respond to potential disturbances and deviations in operating setpoints. This paper demonstrates data-driven methods for predicting the distribution and transient of temperatures and heat fluxes at selected components and regions in a 37-HP system, based on which the optimal control actions in response to changes in user-defined setpoints can be found. We present the development and validation of linear state-space model, feedfoward, and recurrent neural networks. Here, we compare the performance of MPCs with different modeling approaches in terms of following setpoints for temperatures and averaged output heat fluxes. The accuracies of the three data-driven models are similar, but the control actions initiated by neural-network-based MPC can better adapt to drastic changes in setpoints yet generate the smallest errors.
There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.
Coordinated control of electric loads can provide valuable grid services, such as frequency regulation. However, due to the nonlinear characteristics of such load ensembles, it is important to systematically analyze their behavior and establish a thorough understanding of undesirable phenomena that can potentially arise. In this paper, we analyze the frequency response of an aggregate control scheme, with the goal of exploring controller performance limits. We show that rapid switching commands can induce oscillations in the power output due to the inherent lockout mechanism of the underlying devices. Here, we demonstrate that highly detailed aggregate models are required to capture such phenomena. Such models enable deeper understanding of the control boundaries and therefore play an important role in avoiding the introduction of undesirable effects on the grid.
To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.
With increasing integrations of large‐scale systems based on permanent magnet synchronous generator wind turbine generators (PMSG‐WTGs), the overall inertial response of a power system will tend to deteriorate as a result of the decoupling of rotor speed and grid frequency through the power converter as well as the scheduled retirement of conventional synchronous generators. Thus, PMSG‐WTGs can provide value to an electric grid by contributing to the system's inertial response through the inherent kinetic energy stored in their rotating masses and fast power converter control. In this study, an improved inertial control method based on the maximum power point tracking operation curve is introduced to enhance the overall frequency support capability of PMSG‐WTGs in the case of large supply–demand imbalances. Moreover, this method is implemented in the CART2‐PMSG integrated model in MATLAB/Simulink to investigate its impact on the wind turbine's structural loads during the inertial response process. Simulation results indicate that the proposed method can effectively reduce the frequency nadir, arrest the rate of change of frequency, and alleviate the secondary frequency dip while imposing no negative impact on the major mechanical components of the wind turbine.