Coordinated Control Strategy Research Articles

Data-Driven FMPC Based Dynamic Coordination Control Strategy for Power-Split Hybrid Electric Bus

Since no engine clutch exists in the power-split system of the hybrid electric bus, the torque variations of the power sources (i.e., the engine, motor, and generator) are easy to cause system shock during mode switching. To yield a better control effect, the model predictive control (MPC) based dynamic coordination control strategy (DCCS) is considered as an effective solution to guarantee the drivability and reliability of the system. However, the existing MPC-based DCCS still have the challenge of model accuracy and computational complexity. Therefore, to solve the problem, a data-driven Fast-MPC(FMPC) based DCCS is proposed for a power-split hybrid electric bus in this paper. Firstly, to ensure the accuracy of the engine dynamic model, a piecewise data fitting method is proposed by analyzing the historical data of the engine. Then, considering the high dimension of the control model (based on the dynamics model of the power-split system), a fast solving method for the quadratic optimization problem of MPC is proposed with respect to the proper assumption and simplification. Finally, to verify the effectiveness of the proposed control strategy, offline simulation and hardware-in-the-loop (HIL) simulation experiments are carried out. Simulation results show that good driving comfort and real-time performance have been achieved. This research is expected to promote the application of MPC in the DCCS and further improve the dynamic performance of the power-split system.

The coordinated control strategy of DC microgrid based on multiple energy storage units

Recently, the DC microgrid (MG) has caught people’s attention because of its simpler control system than the AC microgrid. In this paper, the bus voltage layering control method based on droop control is used for DC microgrid coordination control. According to the working characteristics of the DC bus, the voltage of the DC bus is divided into five sections, corresponding to 5 operating modes respectively. Inside the DC microgrid, each unit converts the operation mode by local control according to voltage amplitude. The photovoltaic power generation unit adopts MPPT control and constant voltage control to adjust the running point tracking, so as to realize changing-over smoothly between voltage regulator mode and MPPT. The battery system adopts a hierarchical operation control method of several batteries on the basis of the state of charge (SOC). To realize the target, which means getting rational active power allocation and SOC equilibrium of each battery unit, as well as different batteries changing-over the charge and discharge modes on the basis of SOC size, so as to adjust the droop curve according to the power size. Finally, the balance between the three objectives of the highest photovoltaic energy utilization rate, energy storage SOC balance, and stable bus voltage is achieved.

Adaptive Coordination Control Based on Alkaline Water Electrolyzers and Battery Driven by Wind Power

With the widespread use of clean energy sources, there has been a growing interest in obtaining green and pure hydrogen from wind power. However, wind energy is characterized by randomness and volatility, which results in a low grid connection rate for wind turbines and makes it difficult to integrate wind energy into the power grid. In this context, this article proposes an adaptive coordinated control strategy considering the wind turbine, alkaline electrolyzer, battery, and local loads. Using the DC bus voltage as the connecting signal, adaptive power control and voltage control methods are developed for the wind turbine, energy storage battery, and electrolyzer to achieve power matching in the system. Finally, the proposed research results are validated through Matlab/Simulink simulation module.

Coordinated control of AFS and DYC for electric vehicles with mechanical elastic electric wheels considering the roll effects and uncertain parameters

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.

Overview of Stability and Coordination Control for MVDC Traction Power Supply System

The traditional 25KV electric railways have various problems such as power quality issues, passing neutral sections, complex storage, and utilization of regenerative braking energy, the medium‐voltage direct current (MVDC) traction power supply system (TPSS) may be a potential alternative electric railway mode and can be freely accessed to the renewable energy and energy storage systems, which is an important development direction of the future low‐carbon RES. This paper first describes the structure and characteristics of the current Railway Electrification System (RES), and then analyzes and summarizes the current research status of the MVDC TPSS topology structure, modeling method, stability criterion, and analysis method and coordination control strategy. Finally, combined with the research status, the development direction of the MVDC TPSS has prospected. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

CNN‐LSTM based power grid voltage stability emergency control coordination strategy

AbstractThe occurrence of cascading failure and small probability contingency in hybrid AC/DC power grid aggravates the mismatch risk of traditional emergency control with offline‐predetermination–online‐practice (ODOP) mode. To ensure the voltage stability of power grid under large disturbances, this paper proposes a voltage stability emergency control coordination strategy based on convolutional neural network (CNN) and long short‐term memory (LSTM) network. First, the mismatch mechanism of traditional emergency control is revealed under the variation of operating conditions. Second, the start criterion of complementary emergency control with ODOP mode is proposed, and the CNN‐LSTM network is established to quantitatively evaluate the voltage stability margin. Finally, the emergency control sensitivity index is proposed to predict the stability margin enhancement under alternative emergency control measures, and the optimal ODOP‐based emergency control strategy is determined to coordinate multiple voltage stability emergency control measures. Case studies are performed in the actual Northwest China local region hybrid AC/DC power grid with voltage instability problems, and the simulation results verify the effectiveness of the proposed method.

A yaw stability-guaranteed hierarchical coordination control strategy for four-wheel drive electric vehicles using an unscented Kalman filter

A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller.

Power pool‐based supervisory coordinate control strategy for DC microgrids

SummaryStandalone DC microgrids are progressively becoming explored in the modern power system. Proper voltage regulation and effective power distribution among sources are the main challenges for DC microgrid operation. A supervisory coordinated control strategy for proportionate power‐sharing and enhanced voltage regulation is presented in this paper to overcome these challenges. The formation of multiple power pools on the basis of available power from each renewable energy source (RES) is a crucial element of this approach. It modifies the power sharing between power pools and individual sources based on the amount of power available in the sources. In addition, for effective power management, it coordinates the operation of all power pools with all RESs. Also, by reducing the charging and discharging cycle of energy storage systems (ESSs), it is possible to make effective use of ESSs. The proposed control adapts to intermittent RESs and changes in the microgrid network. The proposed method's capabilities are validated by experimental results.

Coordinated control of longitudinal and lateral movements considering dynamics for distributed drive electric vehicle platoon on curved roads

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader’s state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle’s dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon.

A looper-thickness coordinated control strategy based on ILQ theory and GA-BP neural network

A looper-thickness coordinated control strategy based on ILQ theory and GA-BP neural network

A Comprehensive Regional Traffic Coordination Control Strategy Integrated the Short-Term Traffic Flow Identification and Prediction

Traffic congestion caused by the rapid growth of vehicles and unadaptable signal control has become a major constraint on traffic efficiency and travel experience. However, the existing studies have primarily focused on a particular aspect of traffic behaviors, without considering the systemic and interconnected nature of traffic congestion. Hence, a comprehensive strategy integrating monitoring, prediction, and coordinated control at regional intersections is proposed to improve traffic efficiency. First, an intelligent algorithm is designed to identify and predict traffic condition information. Then, the intersections with similar traffic behaviors and higher relevancy are divided into the same subzone, and a multiobjective optimization model is proposed to improve traffic capacity and green time utilization. Furthermore, the timing is modified according to the temporal and spatial characteristics of the oversaturation. Simulation studies in long-term and short-term traffic congestion scenarios are designed to verify the performance of the proposed strategy. Compared with the existing multitime timing method and a newly formed reinforcement learning control method, the results show that the average delay of the proposed strategy is 4.945% and 6.737% inferior to the outstanding strategies.

Network-Wide Coordinated Control Based on Space-Time Trajectories

Under a connected environment, demand information such as vehicle routes in urban networks is accessible. Based on space-time trajectories, this article proposes a real-time network-wide coordinated control strategy within a rolling horizon scheme. A traffic demand framework involving route and arrival and departure times at each intersection is introduced to describe the space-time trajectories of vehicles in the network. In the coordinated control optimization algorithm, the following two steps are iterated: first, the departure time of vehicles with minimal total delay is optimized by a nonfixed intersection signal control model; then, the arrival time is updated according to the departure time and the link travel time. The intersection signal control strategy is composed of a duration division, vehicle aggregates, and an optimization model, where the signal timing plan with minimal delay can be obtained via the departure time. The simulation results indicate that the proposed approach is capable of optimizing the signal timing plan and decreasing vehicle delay given the demand information of vehicles.

Research on Low-Frequency Stability under Emergency Power Supply Scheme of Photovoltaic and Battery Access Railway Traction Power Supply System

Photovoltaics and batteries can be connected to a traction power supply system through a railway power conditioner (RPC) to switch between different control strategies. This can address power quality issues or provide emergency traction for locomotives that unexpectedly lose power and even break through traditional energy barriers in the railway field, achieving a low-carbon power supply for railway energy, and a mutual backup with substations. However, methods to coordinate the control strategies of PV and the battery locomotive traction have not been clearly revealed, nor has the actual stability of the system. In this study, to address the above issues, an emergency power supply scheme is proposed for the first time that utilizes a dual-mode RPC inverter combined with a coordinated control strategy for the PV and battery, achieving the traction of locomotives. In addition, a one-dimensional impedance model was established for the PV system, battery system, locomotive (CRH3), and RPC projected onto the dq coordinate system, and the critical amplitude margin (CAM) was defined to quantitatively analyze the sensitivity and laws of different parameters concerning the low-frequency stability of the system. At the same time, impedance ratios and passive criteria were used to reveal the stability mechanism, and parameter adjustment criteria and design suggestions were put forward. Finally, the feasibility of the emergency power supply scheme of the “PV–battery locomotive network” coupling system and the correctness of the low-frequency stability study were verified using the Starsim semi-physical experiment platform.

Coordinate Fault Ride-Through Strategy for Connection of Offshore Wind Farms Using Voltage Source-Converter-Based High-Voltage Direct-Current Transmission under Single Polar Fault.

In a system where wind farms are connected to the grid via a bipolar flexible DC transmission, the occurrence of a short-time fault at one of the poles results in the active power emitted by the wind farm being transmitted through the non-faulty pole. This condition leads to an overcurrent in the DC system, thereby causing the wind turbine to disconnect from the grid. Addressing this issue, this paper presents a novel coordinated fault ride-through strategy for flexible DC transmission systems and wind farms, which eliminates the need for additional communication equipment. The proposed strategy leverages the power characteristics of the doubly fed induction generator (DFIG) under different terminal voltage conditions. By considering the safety constraints of both the wind turbine and the DC system, as well as optimizing the active power output during wind farm faults, the strategy establishes guidelines for the wind farm bus voltage and the crowbar switch signal. Moreover, it harnesses the power regulation capability of the DFIG rotor-side crowbar circuit to enable fault ride-through in the presence of single-pole short-time faults in the DC system. Simulation results demonstrate that the proposed coordinated control strategy effectively mitigates overcurrent in the non-faulty pole of flexible DC transmission during fault conditions.

Research on Coordinated Control Strategy of DFIG-ES System Based on Fuzzy Control

As the penetration rate of wind power systems is rising, which causes the overall system’s inertia to decline, the power system’s capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system’s level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective.

An improved fault control strategy for virtual synchronous generator with the coordination of STATCOM during unbalanced fault

AbstractThe virtual synchronous generator (VSG) is a good solution for stabilizing the power system with high penetration of renewable energy. However, in case of serious unbalanced voltage disturbance/fault, the conventional VSG may lose voltage, inertia, and damping support characteristics to the grid and even can cause disconnection of renewable energy. This paper proposes an improved fault control strategy for VSG with the coordination of STATCOM. The proposed method can provide sufficient voltage support while keeping continuous system inertia and damping support under severe unbalanced fault. In the paper, an improved VSG and STATCOM control topology based on positive and negative sequence current control are first proposed so as to keep the damping and inertia support to grid during the grid fault. Secondly, the voltage support control method for the VSG with improved topology during unbalanced fault is introduced, which can achieve multiple control objectives, in terms of voltage support, current limitation, and active power output simultaneously. Then a coordination control scheme of improved VSG and STATCOM is developed so as to optimize the maximum control objectives in all possible scenarios, especially in the case of severe unbalanced fault. Finally, the effectiveness of the method is verified by using the MATLAB/SIMULINK simulation platform.

An efficient intelligent energy management strategy based on deep reinforcement learning for hybrid electric flying car

Hybrid electric flying cars hold clear potential to support high mobility and environmentally friendly transportation. For hybrid electric flying cars, overall performance and efficiency highly depend on the coordination of the electrical and fuel systems under ground and air dual-mode. However, the huge differences in the scale and fluctuation characteristics of energy demand between ground driving and air flight modes make the efficient control of energy flow more complex. Thus, designing a power coordinated control strategy for hybrid electric flying cars is a challenging technical problem. This paper proposed a deep reinforcement learning-based energy management strategy (EMS) for a series hybrid electric flying car. A mathematical model of the series hybrid electric flying car driven by the distributed hybrid electric propulsion system (HEPS) which mainly consists of battery packs, twin turboshaft engine and generator sets (TGSs), 16 rotor-motors, and 4 wheel-motors is established. Subsequently, a Double Deep Q Network (DDQN)-based EMS considering ground and air dual driving mode is proposed. A simplified method for the number of control variables is designed to improve exploration efficiency and accelerate the convergence speed. In addition, the frequent engine on/off problem is also taken into account. Finally, DDQN-based and dynamic programming (DP)-based EMSs are applied to investigate the power flow distribution for two completely different hypothetical driving scenarios, namely search and rescue (SAR) scenarios and urban air mobility (UAM) scenarios. The results demonstrate the effectiveness of the DDQN-based EMS and its capacity of reducing the computation time.

Optimal Coordinated Control of Active Front Steering and Direct Yaw Moment for Distributed Drive Electric Bus

This paper suggests a hierarchical coordination control strategy to enhance the stability of distributed drive electric bus. First, an observer based on sliding mode observer (SMO) and adaptive neural fuzzy inference system (ANFIS) was designed to estimate the vehicle state parameters. Then the upper layer of the strategy primarily focuses on coordinating active front steering (AFS) and direct yaw moment control (DYC). The phase plane method is utilized in this layer to provide an assessment basis for the switching control safety of AFS and DYC. The lower layer of the strategy designs an integral terminal sliding mode controller (ITSMC) and a non-singular fast terminal sliding mode controller (NFTSMC) to obtain the optimal additional front wheel steering angle to improve handling performance. A fuzzy sliding mode controller (FSMC) is also proposed to obtain additional yaw moment to ameliorate yaw stability. Finally, the strategy proposed in this paper is subjected to simulation testing and compared with the performance of AFS and DYC systems. The proposed strategy is also evaluated for tracking errors in sideslip angle and yaw rate under two conditions. The results demonstrate that the proposed strategy can effectively adapt to various extreme environments and improve the maneuvering and yaw stability of the bus.

Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

In order to improve the stability and economy of 4WID-4WIS (four-wheel independent drive—four-wheel independent steering) electric vehicles in trajectory tracking, this paper proposes a trajectory tracking coordinated control strategy considering energy consumption economy. First, a hierarchical chassis coordinated control architecture is designed, which includes target planning layer, and coordinated control layer. Then, the trajectory tracking control is decoupled based on the decentralized control structure. Expert PID and Model Predictive Control (MPC) are employed to realize longitudinal velocity tracking and lateral path tracking, respectively, which calculate generalized forces and moments. In addition, with the objective of optimal overall efficiency, the optimal torque distribution for each wheel is achieved using the Mutant Particle Swarm Optimization (MPSO) algorithm. Additionally, the modified Ackermann theory is used to distribute wheel angles. Finally, the control strategy is simulated and verified using Simulink. Comparing the control results of the average distribution strategy and the wheel load distribution strategy, it can be concluded that the proposed coordinated control not only provides good trajectory tracking but also greatly improves the overall efficiency of the motor operating points, which enhances the energy economy and realizes the multi-objective coordinated control of the chassis.

A coordinated control scheme for VSC-MTDC system with multiple renewable energy sites based on voltage margin and adaptive drooping control

This paper designs a novel coordinated control scheme to improve the control performance of voltage source converters (VSC) based multi-terminal high voltage direct current (MTDC) transmission network by merging the benefits of adaptive drooping and voltage margin controllers. In this proposed scheme, the droop coefficient is used to manage the DC voltage and maintain power sharing by shifting the control modes of the rectifiers and inverters in the presence of considerable power disturbances. The droop coefficient is planned based on the stability limit of DC voltage. The performance of the designed control scheme is compared to that of existing coordinated control and voltage margin droop control approaches. A ±400 kV five-terminal VSC-MTDC network is developed in PSCAD to verify the planned control method. The simulation results indicate that the proposed control scheme can achieve good performance for controlling the DC voltage and maintaining active power in the MTDC system.