Control Scheme For Regulation Research Articles

Comprehensive frequency regulation control strategy of thermal power generating unit and ESS considering flexible load simultaneously participating in AGC

The strategy for frequency modulation control of energy storage assisted AGC (automatic generation control) systems with flexible loads was looked into from the viewpoint of source charge interaction in order to optimize the problem of single cell storage with flexible loads on the load side with slower energy storage forces in less fluctuating grids. In order to take advantage of both system stability and energy storage safety, a battery energy storage system is configured on the power side, and a linear regression function model is used to characterize its maximum power constraint in different needs as well as different levels of charge. Upper and lower limits of the total controllable force were determined by simultaneously invoking flexible loads on the load side and polymerizing the material using a method under central control. In order to extend the useful life of energy storage while also solving the frequency problem more quickly and effectively, different regions are divided using the frequency deviation signal, which is regulated by various modes of action in various regions. Four frequency modulation scenarios with and without flexible loads and energy storage systems engaged in AGC frequency modulation were compared using MATLAB/SIMULINK for simulation validation. The findings demonstrated that the suggested control technique may improve frequency modulation performance and lower the lifetime loss of energy storage.

Research on the Frequency Regulation Strategy of Large-Scale Battery Energy Storage in the Power Grid System

Driven by the carbon peaking and carbon neutrality target, the large-scale grid-connected of renewable energy such as wind and solar has increased, and the volatility and randomness have posed new challenges to the stability of the power grid frequency. In this case, battery energy storage is a grid auxiliary resource with fast response and adjustable parameters, which can provide frequency support for the grid system in a short period. This paper studies the frequency regulation strategy of large-scale battery energy storage in the power grid system from the perspectives of battery energy storage, battery energy storage station, and battery energy storage system, respectively. First of all, the droop control based on logistic function and the virtual inertia control based on piecewise function are proposed for battery energy storage frequency regulation, which improves the performance of battery energy storage power output effectively. Second, the weighting factor is set according to the current battery charge to achieve the most optimal distribution of frequency regulation power for each battery pack in the battery energy storage station. In the end, a control framework for large-scale battery energy storage systems jointly with thermal power units to participate in system frequency regulation is constructed, and the proposed frequency regulation strategy is studied and analyzed in the EPRI-36 node model. The results of the study show that the proposed battery frequency regulation control strategies can quickly respond to system frequency changes at the beginning of grid system frequency fluctuations, which improves the stability of the new power system frequency including battery energy storage. In addition, this paper also provides a certain reference for the construction of the new power system dispatching that integrates “Generation-Grid-Load-Storage” in the future.

An Improved Adaptive Load Shedding Control Strategy for Primary Frequency Regulation of Wind Power Generation System

With the continuous improvement of the proportion of wind power generation, the volatility and uncertainty of wind power pose a serious threat to the stable operation of the power system. However, the traditional primary frequency regulation strategy does not fully consider the influence of wind turbine inertia. In addition, it may cause the secondary frequency drop if the instantaneous power of the wind turbine drops too much during the speed recovery. To solve this problem, this paper proposes an adaptive primary frequency regulation strategy for the mobile load shedding power tracking curve. Under this strategy, by controlling the output active power reference value, the kinetic energy stored in the rotor is fully released to support the grid frequency for a short time. Then, in the process of rotor speed recovery, the frequency secondary drop can be alleviated by moving the load-shedding power tracking curve. Finally, the simulation results in MATLAB/Simulink verify the rationality of the proposed adaptive load-shedding control strategy.

Real-time optimization of train regulation and passenger flow control for urban rail transit network under frequent disturbances

Urban rail transit networks face frequent delays and oversaturation resulting from unexpected disturbances and high demands during rush hours, which inevitably leads to unstable train operations, poor service experiences, and potential accidents on crowded platforms. This paper explores the train regulation and passenger flow control strategies for large-scale urban rail transit networks. Specifically, we develop a mixed-integer nonlinear programming (MINLP) formulation to improve the punctuality and regularity in train operations, reduce the passenger waiting time, and alleviate the passenger flow burden of platforms, where the dynamic coupling interactions among network-wide passengers, trains and stations are systematically considered. To reduce the computational complexity and satisfy real-time requirements, we particularly devise a real-time optimization approach based on iterative nonlinear programming (INP) combined with Lagrangian relaxation (LR) under the rolling horizon (RH) framework, which effectively disposes of the intractable nonconvexity and constructs the desirable properties of decomposability and parallelism. The original problem is transformed into a sequence of line-level subproblems that can be solved quickly, so as to circumvent the computational burden. Moreover, to verify the effectiveness of our approach, we apply it to address numerical examples on a simplified network and the real-world Beijing Subway network. The realistic case studies illustrate that the proposed approach can reduce the train deviation from the original timetable, the passenger waiting time, and the passenger accumulation risk on the platform, by around 84.92%, 30.34% and 61.42% in comparison with the common rule-based strategy. Additionally, the computation time for the realistic large-scale experiment is acceptable for a real-time implementation. • The real-time train regulation and passenger flow control strategy is designed. • The dynamic coupling interaction among passengers and trains is considered. • A mixed-integer nonlinear programming model is formulated. • A real-time optimization approach is designed based on iterative nonlinear programming.

Research on the Primary Frequency Regulation Control Strategy of a Wind Storage Hydrogen-Generating Power Station

Wind curtailment and weak inertia characteristics are two factors that shackle the permeability of wind power. An electric hydrogen production device consumes electricity to produce hydrogen under normal working conditions to solve the problem of abandoning wind. When participating in frequency regulation, it serves as a load reduction method to assist the system to rebuild a power balance and improve the wind power permeability. However, due to its own working characteristics, an electric hydrogen production device cannot undertake the high-frequency component of the frequency regulation power command; therefore, an energy storage device was selected to undertake a high-frequency power command to assist the electric hydrogen production device to complete the system frequency regulation. This paper first proposes and analyzes the architecture of a wind storage hydrogen-generating station for centralized hydrogen production with a distributed energy storage, and proposes the virtual inertia and droop characteristic mechanism of the wind storage hydrogen-generating station to simulate a synchronous unit. Secondly, an alkaline electrolysis cell suitable for large-scale engineering applications is selected as the research object and its mathematical model is established, the matching between different energy storage devices and their cooperation in power grid frequency regulation is analyzed, and a super capacitor is selected. A control strategy for the wind storage hydrogen-generating power station to participate in power grid frequency regulation with a wide time scale is then proposed. Using the first-order low-pass filter, the low-frequency component of the frequency regulation power command is realized by an electric hydrogen production device load reduction, and a high-frequency component is realized by the energy storage device. Finally, the effectiveness and rationality of the proposed control strategy are verified by establishing the simulation model of the wind storage hydrogen-generating power station with different initial wind speed states, comparing the system frequency dip values under the proposed multi-energy cooperative control strategy and a single energy device control strategy.

A unified control strategy for voltage regulation and congestion management in active distribution networks

The advent of distributed renewable energy sources (DRESs) has led to a series of technical issues affecting the secure and reliable operation of active distribution networks. Among them, under-/overvoltages, current overload, and voltage unbalance can be considered as the most important problems limiting the increase of DRES penetration. In this paper, a new control architecture is proposed to overcome these issues using the reactive power of DRESs and the active/reactive power of distributed battery energy storage systems (DBESSs). Its distinct feature is the implementation in the symmetrical components domain, allowing the efficient decoupling between under-/overvoltage and voltage unbalance mitigation techniques. Furthermore, a central controller is introduced to improve the system performance in terms of reduced network losses and effective DBESS utilization by coordinating the response of DRESs and DBESSs. The validity of the proposed control strategy is evaluated by performing time-domain and time-series simulations on the IEEE European LV test feeder. • Unified control strategy for unbalanced low voltage distribution grids. • Voltage regulation, voltage unbalance mitigation and congestion management control. • Distributed generation and energy storage are used as the main control units. • Implementation of the control schemes in the symmetrical components domain. • Performance evaluation by conducting time-domain and quasi-static simulations.

Nonlinear MPC-Based Acceleration Slip Regulation for Distributed Electric Vehicles

To address the problem in which wheel longitudinal slip rate directly affects the dynamics and handling stability of a vehicle under driving conditions, front and rear dual-motor four-wheel drive electric vehicles (4WD EVs) were selected as the research object in this study. An acceleration slip regulation (ASR) control strategy based on nonlinear model predictive control (NMPC) is proposed. First, the vehicle dynamics model and the Simulink/CarSim co-simulation platform were built. Second, an ASR controller with intervention and exit mechanisms was designed with the control objective of tracking reference speed or optimal slip rate. Then, considering the problem that the left and right wheels could not freely distribute torque under the condition of a split road surface, the motor output torque was determined in accordance with the wheel with the larger slip rate to enhance passibility. Finally, on the basis of the built Simulink/CarSim co-simulation platform, slip rate control simulation experiments were performed on a snow-covered road, a wet asphalt road, a docking road, and a split road. The designed controller can better track target slip rate and it exhibits better dynamic performance and stability than the method with PID control under different road conditions, especially under low speed and low adhesion road conditions, and its robustness can also meet the requirements.

Control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delays

This paper presents a control strategy for plasma glucose regulation in the presence of input with unknown time delay (constant or variable) and meal disturbances in the system. The control scheme proposed is an observer-based state feedback controller. The observer achieves to estimate the state vector even if the control input in the system presents an unknown time delay in the input. The main advantage of the controller is that the gains are selected so the control law will provide positive values that ensure an implementable case. The observer-based control scheme is validated considering different disturbance and unknown delays scenarios, in both cases, glucose regulation is achieved, rejecting meal disturbances in 4 hours and avoiding hypoglycemic episodes.

Online Storage Technology of the Separate Sewage System: Demonstration Study in a Typical Plain River Network City

Due to the high underground water level, frequent rainfall, and large amounts of infiltration and inflow (I/I) into the sewage system, a city in the plain river network region had to face a series of problems caused by the high water-level operation of the drainage system. Suzhou, a city in the Yangtze River Delta region of China, can be a representative of cities in plain river networks, where this research was carried out. The amount of I/I into the sewage system was evaluated, and the storm water management model (SWMM) was used to further calculate the sewer water storage capacity under dry and wet weather with multi-year average rainfall. Based on the offline model calculation and artificial experiences, the rule-based online regulation and storage real-time control strategy (RTC) is verified, and the online regulation and storage intelligent scheduling demonstration is carried out in the central-city district of Suzhou. The results showed that the infiltration in dry weather accounted for about 20–25% of the total collected wastewater; in wet weather (36 mm precipitation), the extraneous water induced by I/I peaked at 73.64%. The collaborative control of regional multi-stage pumping stations through RTC of the sewage system can effectively avoid the high water-level operation caused by peak sewage flows on dry days. In combination with rainfall forecasting, the coordinated control of plants and pumping stations to pre-empty the sewer pipelines prior to rainfall can, to some extent (up to 35 mm of rainfall in this study), cope with the increase in I/I induced by rainfall.

Approximate Output Regulation of Discrete-Time Stochastic Multiagent Systems Subject to Heterogeneous and Unknown Dynamics

The output regulation approach has been extensively applied for the cooperative control of heterogeneous multiagent systems (MASs) with known dynamics, but rarely for MASs subject to stochastic and unknown dynamics. One challenging problem is that it is still unclear how to construct the regulator equations of stochastic MASs with unknown dynamics for dynamic exosystem compensation. Toward this end, this article develops a novel distributed control scheme for the approximate output regulation of discrete-time stochastic MASs subject to heterogeneous and unknown nonlinear dynamics. A distributed observer is designed for the exosystem-state estimation of agents, based upon which nonlinear regulator equations are constructed for the feedforward control design, thereby achieving distributed exosystem compensation. A high-order neural network is deployed to approximately solve the nonlinear regulator equations with unknown nonlinearity, and an adaptive control law is developed for the approximate output regulation of discrete-time stochastic MASs. Stability analysis shows that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness and efficiency of the proposed control law.

Fuzzy Control Strategy Applied to an Electromagnetic Frequency Regulator in Wind Generation Systems

This paper presents the implementation of a fuzzy control strategy for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in wind power generation. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate, and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial, and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It considers the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which have several limitations that are widely discussed in the literature. This strategy also limits the EFR’s performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

Frequency regulation control strategy of wind farms based on temporal and spatial uncertainty

The Frequency regulation capability of a double-fed induction generator (DFIG) in a large wind farm is influenced by its operating state and installation position, as well as the wind speed (size and direction) at which it is located, resulting in temporal and spatial uncertainties. First, to simplify control difficulty and optimize frequency regulation increment allocation, model predictive control(MPC) and k-means clustering method are used for spatial optimal grouping of DFIG in wind farms based on the uncertainties of DFIG installation location and wind direction. Second, a timing coordinated control strategy is proposed based on the uncertainties of the DFIG operation state and wind speed. The strategy allows the DFIG to enter and exit the frequency regulation system based on wind speed, active power increment, and sustainable time and then reduces the negative effect of the DFIG exiting the frequency regulation system. Finally, based on temporal and spatial optimization, a coordinated control strategy based on temporal and spatial uncertainties is proposed to optimize the overall frequency regulation capability of wind farms. Simulation examples show that the time–space coordinated control strategy treats each group as an independent DFIG control, which not only simplifies the control problem but also fully utilizes the DFIG frequency regulation capacity and effectively improves the system frequency characteristics.

A Novel Nonlinear Quadrotor Attitude Regulation with Disturbances and Preassigned Convergences

This article develops a novel unknown system dynamic estimator-based funnel control scheme for nonlinear quadrotor attitude regulation with preassigned convergence subject to parametric uncertainties and external perturbations. An invariant manifold equipped with first-order filtering is established. To online identify the lump disturbances, an unknown system dynamic estimator is employed with a simple formula, which need a lower computation burden. Based on aforementioned estimator, a novel funnel control via utilizing funnel variable is investigated, where an exponentially decaying funnel function is preset with a prior preassigned convergence for regulation angle error. The angle tracking errors are proved to be ultimately uniformly bounded, and angle regulation error can evolve within the preset funnel boundary. Simulation results demonstrate the effectiveness of the developed control scheme.

Research on coordinated control of flexible on-load voltage regulator and STATCOM

Traditional on-load tap changer (OLTC) has problems such as easy oscillation and low regulation precision in the voltage regulation control of the power system. In order to seek a speedy and stable voltage control strategy, a flexible on-load voltage regulator is studied in this paper. A stepless energy take-off winding is set on the high-voltage side to connect the back-to-back converter, and the winding side of the converter is used to realize reactive power compensation. The grid side of the converter can realize continuous voltage regulation. A coordinated voltage regulation control strategy between flexible on-load voltage regulator and static synchronous compensator (STATCOM) is proposed. The tap positions of the flexible on-load voltage regulator and the reactive power sent by the STATCOM are used as reference signals to ensure sufficient capacity of the compensator and reduce the number of tap actions. The strategy proposed in this paper is compared with the voltage regulation method based on the traditional voltage regulation devices, the effectiveness of the strategy is demonstrated by some simulations based on Matlab/Simulink.

Effects of factors of self-regulation vs. factors of external regulation of learning in self-regulated study.

Since the mid-20th century, the study of Self-Regulated Learning (SRL) has aimed to identify the distinctive characteristics that enable individuals to acquire new knowledge and skills under their control. The theory of Internal Self-Regulation vs. External-Regulation in Learning (SRL vs. ERL; 2017) has postulated that a large number of self-regulatory variables are mediated by regulated/non-regulated or dysregulated features of the context. After signing their informed consent, a total of 616 university students completed validated instruments of SRL vs. ERL, behavioral regulation (SRB), regulatory teaching (RT), and metacognitive study control strategies (SRS). Using an ex-post facto design and correlation, regression, structural equation model and mediation analyses, the present research aimed to establish multicausal predictive relationships among the analyzed variables. Results indicated positive predictive effects between the external regulation variables on the self-regulation variables in learning [regulation (SRL)/non-regulation (NRL)/dysregulation (DRL)]; as well as positive predictive effects between SRL on SRB, RT and metacognitive SRS. Additionally, external regulation (ERL) not only predicted but mediated numerous relations among the variables studied. Other findings and important considerations for future research in the field of self-regulation are discussed.

A g-Normal fuzzy relational hybrid model based control design for hybrid systems with continuous-valued inputs

In this paper, the indirect adaptive control of a special class of hybrid systems is proposed. The proposed control scheme is based on g-normal fuzzy relational hybrid models (g-Normal FRHMs) in which the model of both plant and controller are proposed to be modeled by FRHM structure. There is an assumption on control action to be of continuous-valued type. In order to design a control scheme for both regulation and reference tracking, an additional simplifying assumption is considered. Indeed, when the tuning mechanism is extracted, it is assumed that the defuzzifier of the controller’s model and the fuzzifier of the plant’s model are approximately inverse of each other and can be simplified. The proposed control approach is then applied to a DC-DC buck converter as our case study. The simulation results show that the proposed adaptive control based on g-Normal FRHM can regulate and also track the reference input in the presence of both load and supply voltage disturbance.

Stepping quantum genetic algorithm-based LQR control strategy for lateral vibration of high-speed elevator

Abstract To effectively restrain the lateral vibration caused by the guide rail excitation and improve the ride comfort of the car system, a state-weighted linear quadratic regulator (LQR) control strategy is proposed. Firstly, based on the active control model of the 4-DOF car system with actuators distributed diagonally along the center of the car frame, an LQR controller for lateral vibration of high-speed elevator car systems is designed. Furthermore, in view of the tedious and time-consuming of the empirical method to choose state-weighted matrix Q, stepping quantum genetic algorithm (SQGA) is proposed to improve the performance of the controller. Finally, the time-frequency characteristic curves of the lateral vibration acceleration and the vibration displacement of the car system are compared and analyzed by MATLAB to verify the effectiveness of the proposed controller.

Simulation Analysis of Series Regenerative Control Strategy Based on Fuzzy Control

Simulation Analysis of Series Regenerative Control Strategy Based on Fuzzy Control

A robust control scheme for voltage and reactive power regulation in islanded AC microgrids

• An enhanced resilient control strategy for multi-feeder islanded AC microgrids that shares reactive power proportionally and restore load feeder voltages, simultaneously. • One of the important aspects of the proposed control scheme is that, only the voltage magnitude information is required to transfer at each inverter's controller to achieve the two-fold objectives, simultaneously. For this, quadratic optimal problem is formulated based on phasor impedances of all the load feeders in the network, which computes optimal voltage commands. • A novel load estimation scheme to dynamically estimate phasor impedance at each load feeder associated with the voltage and current of that feeder. • Approximation of microgrid system with multiple load feeders to solitary feeder system. The salient property of this approach is to reduce the computationally expensive and communication intensive MG networks into small, sparse, and efficient alternatives, increasing the feasibility of MG network deployment. • Modeling and analysis of microgrid is verified through stability analysis using system wide mathematical small signal model. • Detailed analysis and comparison of the proposed control structure is presented under the effect of load and transmission line impedance variation at different points for the multi-feeder and approximated models of the MG system. In multi-feeder microgrid systems, accurate power sharing and voltage regulation at each load feeder is more challenging than the conventional single-feeder microgrids. This paper presents an enhanced resilient control strategy for multi-feeder micro-network systems by considering the load feeder voltage regulation and power balancing as a quadratic optimization problem. The proposed control strategy is composed of impedance estimator and optimal controller. However, the impedance estimator obtains each load feeder impedance while optimal controller computes the voltage commands for each distributed generator. One of the important aspects of the proposed control scheme is that only the voltage magnitude information is required to transfer at each inverter's controller, with primary aim to achieve the two-fold objectives. This control strategy is also extended to approximate the whole network impedance into solitary load feeder for micro-networks with extensive number of load feeders. This makes the system computationally less burdening. The MATLAB/Simulink and experimental results are obtained under varying load conditions and line parameter uncertainties for radial type multi-feeder microgrid configuration that reflects the effectiveness of the proposed scheme. The verification outcomes prove the proposed method capability of accurate reactive power sharing, regulation of load feeder voltages, and frequency restoration.

Distributed model predictive control for real-time train regulation of metro line based on Dantzig-Wolfe decomposition

This paper aims to propose a novel distributed model predictive control (MPC) scheme for real-time train regulation in urban metro transportation. Particularly, a nonlinear real-time train regulation model is put forward to minimize the timetable deviations and the control strategies for each trainunder the uncertain disturbances, which is then reformulated into a linear optimization model for easy to solve. By regarding each train as a subsystem, we design the distributed MPC algorithm based on the Dantzig-Wolfe decomposition for the train regulation problem, which decomposes the original optimization problem into numerous smaller and less complicated optimization control problems that can be solved independently. Under the distributed mechanism, we regard each train as a local subsystem, which only interacts with the coordinator, ensuring the flexibility and modularity of the control structure. Numerical cases are provided to demonstrate the effectiveness and robustness of the proposed distributed MPC method.