Large Load Variations Research Articles

Optimized Rear Drive Torque Allocation Strategy for Dual-Motor Mining Dump Trucks

This paper takes the dual-motor pure electric mining dump truck as the research object and designs a dual-motor rear-drive torque optimization allocation strategy in view of the problems such as the large load variation of the dump truck and the facts that the motor output torque cannot accurately express the driver’s dynamic intention and that the overall output efficiency of the dual motor is low. The strategy first divides the demand torque of the whole vehicle into two parts, the base torque and the compensation torque, which are determined by the accelerator pedal opening and the motor speed, and the compensation torque is fuzzy-controlled by taking the vehicle speed, the rate of change of the accelerator pedal opening, and the state of the battery charge as inputs. Subsequently, the dual-motor drive torque allocation is optimized using a particle swarm algorithm, with the objective of minimizing power loss in the dual motors. Furthermore, the energy-saving effect of the torque optimization allocation strategy proposed in this paper is compared with that of the traditional torque average allocation strategy under three working conditions: the driving conditions of Chinese dump trucks, the unloaded uphill movement of mining dump trucks, and the fully loaded downhill movement of mining dump trucks. The results show that the average efficiency of the dual-motor drive using the torque optimization allocation strategy is improved by 2.32%, 4.23%, and 2.24%, respectively, and battery energy savings are improved by 0.5%, 0.47%, and 0.24%, respectively.

Catenary Mooring Length Control for Motion Mitigation of Semi-Submersible Floating Wind Turbines

Besides improving the generator torque and blade pitch controller, incorporating additional control actuations, such as a vibration absorber and active ballast, into the floating offshore wind turbine (FOWT) system is also promising for the motion mitigation of FOWTs. This work aims to study the catenary mooring length re-configuration effect on the dynamic behaviours of semi-submersible FOWTs. The mooring length re-configuration mentioned here is achieved by altering the mooring length with winches mounted on the floating platform, which is in a period of minutes to hours, so that the mooring tensions could be adjusted to reduce the aerodynamic load induced platform mean pitch. Control designs for both single mooring line and multiple mooring lines have been described and studied comparatively. In order to assess the motion mitigation performance of the proposed mooring line length re-configuration methods, fully coupled numerical simulations under different environmental cases have been conducted. Results indicate that the catenary mooring length re-configuration is able to reduce the platform pitch motion by up to 15.8% under rated condition, while careful attention must be paid to the scenarios where the catenary moorings become taut, which may lead to large load variations.

Input channel gain adaptive active disturbance rejection control based on robust adaptive finite‐time parameter estimation

AbstractThis paper presents a novel input channel gain adaptive active disturbance rejection control (ICGA‐ADRC) framework for the nonlinear control system with large load variation. This paper first introduces the design of ICGA‐ADRC and proves its stability through the rigorous Lyapunov approach. Subsequently, the proposed controller is simulated by subjecting it to a nonlinear mass‐spring‐damping system characterized by significant load variations. Furthermore, the position‐tracking performance of the controller is tested experimentally by using a motor‐driven pendulum system with various loads. Finally, the results of both simulations and experiments show that the ICGA‐ADRC is capable of compensating for system model uncertainty and external perturbation while accurately estimating the system input channel gain in real‐time. The innovation of this paper is that the robustness of active disturbance rejection control (ADRC) to the system load variation is significantly strengthened by integrating robust adaptive finite‐time parameter estimation method into the conventional ADRC control architecture.

High-Performance Megawatt-Scale MVDC Zonal Electrical Distribution System Based on Power Electronics Open System Interfaces

Integrated Power and Energy Systems (IPESs) will play a critical role in supporting future complex electric ship operations. In particular, flexibility is required to exploit shipboard energy sources and storages for multiple ship functions, supporting a highly dynamic electrical power use. To this aim, the Medium Voltage DC (MVDC) electrical power distribution has been conceived to provide requested power flexibility, along with other expected advantages in terms of functional integration and ship design. For complex and high-performance ship power systems, the Zonal Electrical Distribution System (ZEDS) concept has been proposed to maximize MVDC capabilities even under extremely demanding operational conditions. This paper presents a megawatt-scale test facility of a MVDC ZEDS developed and built in Trieste, Italy, based on power electronics open system interfaces. The demonstrator is described in its power and control design architectures and capabilities. In particular, a successful MW-scale test is presented, to show ZEDS capability in supporting large and fast load variations, while maintaining acceptable MVDC voltage tolerances.

System-level modeling, analysis and coordinated control design for the pressurized water reactor nuclear power system

A flexible operation of the nuclear power system (NPS) over a large operating range is emphasized when it is used as marine power. The current study exposes two bottlenecks in this field: (1) lack of the system-level high-quality dynamic model whose structure is simple enough for control design limits the implementation of the model-based controller; (2) nonlinearity problem due to the large load variation poses challenges for NPS to maintain its performance over a wide operating range. For these reasons, a first-hand simplified nonlinear model is developed and validated for the pressurized water reactor nuclear power system (PWRNPS) through mixed modeling methods of mechanism analysis and data identification. An optimal 50% full-power compromise steady-state operation scheme is first proposed for PWRNPS to balance its primary coolant temperature and secondary steam pressure. Using the simplified nonlinear model as the predictive model, a novel nonlinear model predictive controller (NMPC) is designed to realize the wide-range flexible operation of PWRNPS. Especially, an integrating tracking error is innovatively added to the cost function of NMPC to achieve offset-free control. Two groups of simulations on a complex PWRNPS process developed on the MATLAB/SIMULINK platform demonstrate the effectiveness of the proposed methods.

Flexible operation of the pressurized water reactor nuclear power system using multi-model predictive control over a wide nonlinear operating range

The nonlinearity problem caused by the large load variation is a significant challenge for the pressurized water reactor nuclear power system (PWRNPS) to realize its flexible operation over a wide operating range when it participates in peak regulation in the power grid. Thus, this work proposes a novel multi-model predictive control (MMPC) strategy for PWRNPS to address its nonlinearity problem in multivariable control within a wide range of conditions, in which the coupling effect between the average coolant temperature and the steam turbine power is considered. Given the insufficient understanding of the nonlinearity of PWRNPS in previous work, an in-depth investigation in this regard is performed to support the control design, in which the nonlinearity of PWRNPS at the system level is analyzed both on quantitative and qualitative aspects for the first time. Considering the captured uneven nonlinearity distribution of PWRNPS, a new approach integrating hierarchical clustering with gap metric is proposed to guide the decomposition of the operating space of PWRNPS systematically, based on which MMPC is designed to realize the nonlinearity control of PWRNPS under the large load variation. Three types of simulations demonstrate the effectiveness of the proposed methods.

Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.

Optimal Distributed ADMM-Based Control for Frequency Synchronization in Isolated AC Microgrids

To restrict transient dynamics of each distributed generator (DG) in isolated AC micro-grids (MGs), an optimal distributed alternating direction method of multiplier (ADMM)-based control scheme, will be explored for achieving frequency synchronization within a given finite horizon. By introducing the consensus variable, we re-phrase the frequency synchronization problem as a linear quadratic tracking problem under the framework of multi-agent systems. The objective function of each individual DG is defined as the accumulation of quadratic frequency errors and quadratic inputs. Since the consensus variable is also part of the decision variable, more flexibility can be gained in developing the optimal distributed ADMM-based algorithm within a finite horizon. Since the direct extension of ADMM for static optimization can not be fully distributed, a fully distributed ADMM-based control is developed by exploring the optimal distributed control theory in each DG. To validate the performance of the proposed method, real-time simulations on OPAL-RT are conducted to validate the effectiveness of the proposed optimal distributed ADMM-based control strategy even under large load variations and plug-and-play operations of DGs.

Development of a Low-Cost Relay Prototype for Real-Time Power Protection Functions

This paper presents a Raspberry Pi 3B+ based low-cost universal relay able to run power protection functions in real-time. The configurations necessary for this single-board computer to be able to provide real-time response are shown, as for latency tests to verify its response time. An experimental circuit was built to send three-phase fault signals from a Relay tester to the low-cost relay, in order to evaluate its response time to clear faults in comparison to a commercial relay. A neural networks algorithm was developed and executed in real-time by the proposed low-cost relay, which is able to differentiate three-phase faults from transient signals created from large load variations. The results show that the low-cost relay is capable of running simple and complex protection functions within a pre-defined runtime and acceptable precision, compared to a commercial protection relay. However, the sampling frequency the low-cost relay is able to handle is limited. The results have also shown that the low-cost relay meet the requirements for a soft real-time system, which is not ideal for practical power protection systems that require hard real-time systems.

Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station

The growing number of electric vehicles (EVs) affects the national electricity system in terms of power demand and load variation. Turning our attention to Italy, the number of vehicles on the road is 39 million; this represents a major challenge, as they will need to be recharged constantly when the transition to electric technology is complete. If we consider that the average power is 55 GW and the installed system can produce 120 GW of peak power, we can calculate that with only 5% of vehicles in recharging mode, the power demand increases to 126 GW, which is approximately 140% of installed power. The integration of renewable energy sources will help the grid, but this solution is less useful for handling large load variations that negatively affect the grid. In addition, some vehicles committed to public utility must have a reduced stop time and can be considered to have higher priority. The introduction of priorities implies that the power absorption limit cannot be easily introduced by limiting the number of charging vehicles, but rather by computing the power flow that respects constraints and integrates renewable and local storage power contributions. The problem formulated in this manner does not have a unique solution; in this study, the linear programming method is used to optimise renewable resources, local storage, and EVs to mitigate their effects on the grid. Simulations are performed to verify the proposed method.

Mitigating performance deterioration analysis of VC-PEMFC with dead-ended anode by pulsation fuel supplying mode

Hydrogen starvation and water flooding are two principal factors resulting in performance deterioration of the proton exchange membrane fuel cell stack at the dead-end anode. This paper proposes a novel hydrogen supply mode called the pulsation mode aimed at mitigating the problems of performance deterioration in a 10-cell open-cathode vapor chamber proton exchange membrane fuel cell stack to increase the performance. This method does not require complex equipment and structure, only four controllable solenoid valves are sufficient to generate periodic pulsation inside the anode channels. The experiments were used to validate the effectiveness of the new mode and to compare the effects of different pulsation frequencies on the performance of the stack. A series of parameters such as voltage, power growth rate, and voltage stability index are used to analyze the operating characteristics of the stack. The results show that the periodic pulsations generated by the new mode are potent of increasing the species mass transfer rate within the anode channels, and the species mass transfer rate increases with the increase of pulsation frequency. Meanwhile, selection of a suitable pulsation frequency can effectively improve stack water management and reduce the probability of hydrogen starvation. Finally, the new mode is able to enhance the voltage down valley of the stack under large external load variation. The ohmic resistance of the stack in the new mode has proved to be lower by the current interruption method. Furthermore, it is capable of increasing the net power of the stack by up to 7.71%.

Uncertainty propagation analysis of ionization chamber sensitivity in core power control system of a small PWR

Influence of the uncertainty of ionization chamber sensitivity on the core power control system of a small PWR is studied. Firstly, the model of each link in the core power control system is established with the transfer function. Then, a fuzzy PID controller with simulated annealing algorithm is designed to control the small PWR core power. Finally, under the large range load variation of reactor core, the uncertainty propagation of ionization chamber sensitivity is analyzed based on direct numerical perturbation method. The results show that under 100%FP-15%FP at the rate of 15%FP/min of power in the small PWR, the maximum uncertainty of core relative power is 6.03%. And the maximum uncertainty of coolant outlet temperature deviation and fuel temperature deviation are respectively 14.4% and 14.47%. The uncertainties of the output parameters can be stabilized in a small certain range by fuzzy PID controller.

Numerical and experimental study of feedforward and feedback control for microchannel cooling system

A lumped model for a cooling system incorporated with a microchannel heat exchanger is presented, based on which control strategies with feedforward and feedback controllers are developed. By comparing different control strategies numerically and experimentally, the robustness and accuracy of the proposed physical model are examined, a reliable and efficient control method is designed for achieving high operation efficiency and quick response. Validation of the lumped model shows that the maximum model prediction inaccuracies for wall temperature and pressure drop are within 1%. For the feedforward control system, the response is relatively fast compared with the feedback control system, which has a long settling time due to the heating process of cartridge heaters and heat transfer of the microchannel block. The system with feedforward and feedback control has a fast response and desired steady-state value. For a system undergoing large heat load variations, a scheduled setpoint control method could ensure that the pump work at its middle range of frequency. The numerical and experimental comparisons for different control methods show that the inaccuracies are within 2% for steady states. The application of the feedforward and feedback control strategy could address the deviation of system output from setpoints caused by model inaccuracies, disturbance variations, or perturbations in the operating conditions.

Online PV Smart Inverter Coordination using Deep Deterministic Policy Gradient

Fast and frequent solar power variations present new challenges to modern power grid operation with increasing adoption of photovoltaic (PV) energy. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating active and/or reactive power at the connection point. In this paper, a deep reinforcement learning (DRL) based algorithm is proposed to coordinate multiple SIs. A reward scheme is designed to balance voltage regulation and SI reactive power utilization. The proposed DRL agent for voltage control learns its policy through massive offline simulations and adapts to load and solar variations. The DRL agent results are compared against autonomous Volt-Var control and optimal power flow (OPF) on the IEEE 37 bus feeder and IEEE 123 bus feeder for 8760 different scenarios. The results demonstrate that a properly trained DRL agent can intelligently coordinate different SIs to satisfy grid voltage limits despite large solar and load variations. The DRL agent achieves nearly the optimal performance of OPF by mitigating all voltage violations, while reducing PV production curtailment by 88% compared to the autonomous Volt-Var scheme. Contrary to OPF, the DRL agent can provide a coordination signal in milliseconds without load and solar forecasts and without explicit knowledge of the distribution network model.

Dual-Active-Bridge Model and Control for Supporting Fast Synthetic Inertial Action

This article proposes a dual-active-bridge control to support the fast synthetic inertial action in DC microgrids. First of all, the selection of the isolated DC/DC converter to link an energy storage system with the DC bus in a microgrid is analyzed and the advantages of the dual-active-bridge converter controlled by a single-phase shift modulation justify its selection. An active front-end can be then adapted to connect the DC bus with an AC grid. Secondly, this paper presents the design of a discrete PI controller for supporting fast synthetic inertial action. In particular, a discrete dual-active-bridge model based on the transferred power between both converter bridges, which overcomes the approximations of the output current linearization model, is proposed. Moreover, the article introduces a novel equation set to directly and dynamically tune discrete PI parameters to fulfill the design frequency specifications based on the inversion formulae method. In this way, during the voltage/power transients on the DC bus, the controller actively responds and recovers those transients within a grid fundamental cycle. Since the developed set of control equations is very simple, it can be easily implemented by a discrete control algorithm, avoiding the use of offline trial and error procedures which may lead to system instability under large load variations. Finally, the proposed control system is evaluated and validated in PLECS simulations and hardware-in-the-loop tests.

Nonlinear control of the boost converter subject to unknown constant power load and parasitics

ABSTRACT A robust adaptive controller is developed to regulate the output voltage of the boost converter feeding an unknown constant power load (CPL) and subject to unknown parasitic parameters. The boost converter is a non-minimum phase system that is very prone to the destabilising effects of the negative incremental resistance of the CPL and presents a major challenge in the design of stabilising controllers. An adaptive controller is developed to tightly regulate the output voltage of the converter despite large load variations, parasitic elements in the converter parameters and instability effects induced by the CPL. A systematic procedure is developed to select the adaptive controller gains to achieve a satisfactory output response. Using simulation, the effectiveness of the proposed controller is validated and compared to a recent robust nonlinear controller.

Super-Twisting Algorithm Second-Order Sliding Mode Control of a Bidirectional DC-to-DC Converter Supplying a Constant Power Load.

The Super-Twisting Algorithm is employed to control the voltage of a Bidirectional DC-to-DC Converter of an on-board propulsion system in presence of large load variations. The Bidirectional DC-to-DC Converter is modeled using average value modeling, and it is shown by an alternative approach that the zero dynamics for the converter topology supplying a Constant Power Load are unstable with the DC bus voltage as output, and stable for the inductor current as output. Here, a derivation of the controller gains is presented based on the average model of the system. The gains are described with respect to the inductor current and its bounded perturbations. The performance of the proposed controller is compared to a conventional cascaded PI controller, and exhibits strong robustness with respect to external system variations.

Data Based Linear Power Flow Model: Investigation of a Least-Squares Based Approximation

Linearization of power flow is an important topic in power system analysis. The computational burden can be greatly reduced under the linear power flow model while the model error is the main concern. Therefore, various linear power flow models have been proposed in literature and dedicated to seek the optimal approximation. Most linear power flow models are based on some kind of transformation/simplification/Taylor expansion of AC power flow equations and fail to be accurate under cold-start mode. It is surprising that data-based linearization methods have not yet been fully investigated. In this paper, the performance of a data-based least-squares approximation method is investigated. The resulted cold-start sensitive factors are named as least-squares distribution factors (LSDF). Compared with the traditional power transfer distribution factors (PTDF), it is found that the LSDF can work very well for systems with large load variation, and the average error of LSDF is only about 1% of the average error of PTDF. Comprehensive numerical testing is performed and the results show that LSDF has attractive performance in all studied cases and has great application potential in occasions requiring only cold-start linear power flow models.

Filter Extracted Sliding Mode Approach for DC Microgrids

The advantages offered by DC microgrids, such as elimination of skin effect losses, no requirement of frequency synchronization and high efficiency for power transmission are the major reasons that microgrids have attracted the attention of researchers in the last decade. Moreover, the DC friendly nature of renewable energy resources makes them a perfect choice for integration with DC microgrids, resulting in increased reliability and improved stability. However, in order to integrate renewable energy resources with the DC microgrids, challenges like equal load sharing and voltage regulation of the busbar under diverse varying load conditions are to be addressed. Conventionally, droop control with PI compensation is used to serve this purpose. However, this cascaded scheme results in poor regulation to large load variations and steady state errors. To address this issue, this paper presents a sliding mode control-based approach. Key features of SMC are its ease of implementation, robustness to load variations, and fast dynamic response. The system model is derived and simulated to analyze the stability and performance of the proposed controller. An experimental test bench is developed to demonstrate the effectiveness of SMC against modeled dynamics and is compared with the droop controller. The results show an improvement of 26% and 27.4% in the rise time and settling time, respectively. Robustness of the proposed scheme is also tested by switching it with a step load and an improvement of 40% has been observed.

Analysis of working characteristics of throttle speed regulating valve

In this paper, combined with the working requirements of throttling speed regulation in the circuit, the structure principle and working characteristics of speed control valve in flow control valve are analyzed, the speed regulating performance and characteristics of speed regulating valve in hydraulic system are expounded, It is pointed out that the relationship between speed and load of speed regulating valve is rigid in the process of speed regulating of hydraulic system, that is to say, the speed regulating valve is suitable to be used in the case of large load variation, high speed stability requirements of the occasion.