Power Management Control Research Articles

Coordination Control Strategy for AC/DC Hybrid Microgrids in Stand-Alone Mode

Interest in DC microgrids is rapidly increasing along with the improvement of DC power technology because of its advantages. To support the integration process of DC microgrids with the existing AC utility grids, the form of hybrid AC/DC microgrids is considered for higher power conversion efficiency, lower component cost and better power quality. In the system, AC and DC portions are connected through interlink bidirectional AC/DC converters (IC) with a proper control system and power management. In the stand-alone operation mode of AC/DC hybrid microgrids, the control of power injection through the IC is crucial in order to maintain the system security. This paper mainly deals with a coordination control strategy of IC and a battery energy storage system (BESS) converter under stand-alone operation. A coordinated control strategy for the IC, which considers the state of charge (SOC) level of BESS and the load shedding scheme as the last resort, is proposed to obtain better power sharing between AC and DC subgrids. The scheme will be tested with a hybrid AC/DC microgrid, using the tool of the PSCAD/EMTDC software.

Study of HEV Power Management Control Strategy Based on Driving Pattern Recognition

In this work, an optimized HEV power management fuzzy control strategy is proposed with the aim to further improve the fuel efficiency of the rule-based control strategy and overcome the drawbacks of the conventional control strategies. The driving pattern recognition method is used to classify the driving condition into one of the driving patterns to select proper control algorithm. The dynamic programming solution is used to design the fuzzy control strategies for each driving pattern. The simulation results indicate that by adopting the proposed strategy the fuel efficiency of HEV is improved, especially under complex driving conditions.

Analysis of Control, Learn, and Knowledge Model for Computer Power Management Using Unified Modelling Language

This paper analyzes the human inactivity in the computer system and finds that; the reduction of the efficiency for the computer system power consumption can be ideal by the eligibility of the human inactivity period.This study reviews the self adaptation and control loop concept, and models a framework of a new method.The essential elements and features from these concepts were adapted and applied as an approach to a new implementation of Control, Learn, Knowledge Model.This study establishes a model of the theoretical framework and demonstrates it through a conceptual framework for the CLK.As a result, this study continues with proposing the analysis of CLK using UML model.

Robust fuzzy‐sliding mode based UPFC controller for transient stability analysis in autonomous wind‐diesel‐PV hybrid system

This study presents a comparative study of transient stability and reactive power compensation issues in an autonomous wind–diesel-photovoltaic based hybrid system (HS) using robust fuzzy-sliding mode based unified power flow controller (UPFC). A linearised small-signal model of the different elements of the HS is considered for the transient stability analysis in the HS under varying loading conditions. An IEEE type 1 excitation system is considered for the synchronous generator in the HS, with negligible saturation characteristic, for detailed voltage stability analysis. It is noted from the simulation results that the performance of UPFC is superior to static VAR compensator and static synchronous compensator in improving the voltage profile of the HS. Further, fuzzy and fuzzy-sliding mode based UPFC controller is designed in order to improve the transient performance. Simulation results reflect the robustness of the proposed fuzzy-sliding mode controller for better reactive power management to improve the voltage stability in comparison with the conventional PI and fuzzy-PI controllers. In addition to this, system stability analysis is performed based on eigenvalue, bode and popov for supporting the robustness of the proposed controller.

Optimal Placement of D-STATCOMs into the Radial Distribution Networks in the Presence of Distributed Generations

This paper proposes a combination of Discrete Imperialistic Competition and Nelder-Mead algorithms to solve D-STATCOMs placement optimization problem. It is assumed that optimal number, locations and sizes of D-STATCOMs are determined in radial distribution network while Distributed Generations are previously installed in it. Indeed, we focus on voltage control and concept of reactive power management in annual scheduling time interval. Objective function is defined in terms of network active power losses and voltage stability characteristic at different load levels by appropriate weighting factors. Voltage Stability Index (VSI) is applied to identify the weak buses in radial distribution network. To validate performance and effectiveness of proposed DICA-NM hybrid algorithm, simulation studies are applied on the 30-bus IEEE radial distribution test feeder. Finally, numerical results for some important network variables have been compared in three different case studies.

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper, we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

Chaos oscillator differential search combined with Pontryagin’s minimum principle for simultaneous power management and component sizing of PHEVs

Over the past decade, plug-in hybrid electric vehicles (PHEVs) have found a good reputation in the automotive industry due to the fact that they neatly satisfy the existing tight environmental regulations and fuel economy requirements. Recently, there has been more interest in the design optimization of the PHEV powertrains to improve their operational characteristics to the maximum possible extent. The PHEV powertrains are complicated systems and include different controllers and components which should operate corporately to guarantee the acceptable performance of the vehicle. The reported investigations indicate that improving the performance of PHEVs is a very arduous task because both control strategies and component sizes should be optimized in tandem; however, in most of the previous studies, the focus has been on improving one of the above-mentioned aspects, which does not result in the most efficient design. The main goal of the current study is to take advantage of a bi-level optimization framework which combines the optimizations of both powertrain component sizes and power management controller for a specific PHEV, namely 2012 Toyota plug-in Prius. The bi-level optimizer comprises a chaos-enhanced differential evolutionary algorithm, which is in charge of the component sizing, and a classical optimal control approach based on the Pontryagin’s minimum principle, which optimizes the vehicle power management strategy. A high-fidelity model of the vehicle is developed in the Autonomie software. This high-fidelity model is used to identify the parameters of a reduced model representing the vehicle dynamics by means of the homotopy analysis method, and the resulting model is then employed for the optimization procedure. The results of the numerical experiments indicate that by considering both component sizing and control strategy optimization, a very powerful tool is developed which can significantly improve the total fuel cost (F C ), acceleration time (T acc ), and battery state of charge (SOC) trajectory of the vehicle.

Network interface power management and TCP congestion control: a troubled marriage

Optimizing the trade-off between power saving and Quality of Service in the current Internet is a challenging research objective, whose difficulty stems also from the dominant presence of Transmission Control Protocol (TCP) traffic, and its elastic nature. More specifically, recent works support the possibility of improving energy efficiency of network devices by modulating switching and transmission capacity according to traffic load, whereas TCP traffic is in turn adaptive to the available resources. In a previous work, we have shown that an intertwining exists between capacity scaling approaches and TCP congestion control. In this paper, we investigate the reasons of such intertwining, and we evaluate how and how much the dynamics of the two algorithms affect each other’s performance. More specifically, we will show that such an interaction is essentially due to the relative speed of the two algorithms, which determines the conditions for the successful or unsuccessful coexistence of the two mechanisms.

Regularized MPC for Power Management of Hybrid Energy Storage Systems with Applications in Electric Vehicles

This paper examines the application of Regularized Model Predictive Control (RMPC) for Power Management (PM) of Hybrid Energy Storage Systems (HESSs). To illustrate, we apply the idea to the PM problem of a battery-supercapacitors (SCs) powertrain to reduce battery degradation in Electric Vehicles (EVs). While the application of Quadratic MPC (QMPC) in PM of HESS is not new, the idea to examine RMPC here is motivated by its capabilities to prioritize actuator actions and efficiently allocate control effort, as advocated by recent works in the control and MPC literature. Thorough simulations have been run over standard urban test drive cycles. It is found out that QMPC and RMPC, compared to rule-based PM strategies, could reduce the battery degradation over 70%. It is also shown that RMPC can slightly outperform QMPC in reducing battery degradation. Moreover, RMPC, compared to QMPC, could potentially extend the range of that SCs can be used, thus exploiting the degree of freedom of the powertrain to a larger extent. We also make some discussions on the feasibility issues and tuning challenges that RMPC faces, among others.

Power management using dynamic power state transitions and dynamic voltage frequency scaling controls in virtualized server clusters

Reducing power consumption and maintaining user or application performance criteria is an important goal in virtualized server cluster system design. Achieving this multiple objective requirement in a virtualized environment is a challenge. One of the techniques widely explored in the literature to achieve this goal is the dynamic voltage frequency scaling (DVFS) approach. However, power consumption reduction due to DVFS is far less than what can be achieved with the dynamic power management (DPM) control approach to either switch OFF the server or to transition the server to a low power SLEEP state when not processing application requests. In our work, we have formulated a power optimization problem that meets the dened performance criteria for the workload. We have considered application request response time as the performance metric. The adaptive controller is designed to track application performance in therst step, with dynamic control and batch requests to the best server in order to minimize the power consumption as the second step. In this paper, our aim is to reduce the power consumption of a virtualized server cluster by making use of an adaptive hybrid approach that identies the right server with the best performance for power metric (PPM), DPM with server processor sleep state(s), and DVFS controls. Simulation results show that our hybrid approach using PPM, DPM with sleep state, and DVFS controls is effective, achieves better energy savings, and meets the performance criteria constraint.

Power Control of PV/Fuel Cell/Supercapacitor Hybrid System for Stand-alone Applications

This paper presents modeling and control of Photovoltaic/fuel cell/supercapacitor hybrid power system for stand-alone applications. The hybrid power system uses solar PV array and fuel cell as the main sources. These sources share their power effectively to meet the load demand. The supercapacitor bank is used to supply or absorb load transients. The main control system comprises of MPPT controller for PV system, a DC-DC boost converter with controller for fuel cell system for power management and inverter controller to regulate voltage and frequency. The stand-alone hybrid system aims to provide quality supply to consumers with a constant voltage and frequency. The modeling and control strategies of the hybrid system are realized in Matlab/Simulink.

A Hybrid Energy Efficient Protocol for Mobile Ad Hoc Networks

We proposed an energy conservation technique called Location Based Topology Control with Sleep Scheduling for ad hoc networks. It uses the feature of both topology control approach and power management approach. Like the topology control approach, it attempts to reduce the transmission power of a node, which is determined from its neighborhood location information. A node goes to sleep state based on the traffic condition as that of power management approach. In the proposed scheme, a node goes to sleep state only when its absence does not create local partition in its neighborhood. We preformed extensive simulation to compare the proposed scheme with existing ones. Simulation results show that the energy consumption is lower with increase in the network lifetime and higher throughput in the proposed scheme.

Development of a regenerative braking control strategy for hybridized solar vehicle

This paper focuses on the development of a braking control strategy that allows the best tradeoff between mechanical and regenerative braking on a hybridized vehicle. The research work is part of a project for the development of an automotive hybridization kit aimed at converting conventional cars into Through The Road hybrid solar vehicles. The main aspect of the project is the integration of state-of-the-art components (i.e. in-wheel motors, photovoltaic panels, batteries) with the development of an optimal controller for power management. A mild parallel hybrid structure is obtained by substituting/integrating the rear wheels with in-wheel motors and adding photovoltaic panels and a lithium-ion battery. A hybridizing equipment prototype, patented by the University of Salerno, is installed on a FIAT Grande Punto. A model useful for real-time braking control has been developed, starting from vehicle longitudinal model and considering dynamic weight distribution in front and rear axles and related wheel slipping effects. Different braking strategies have then been investigated, in order to maximize the benefits of regenerative braking.

Design and Optimization of the Power Management Strategy of an Electric Drive Tracked Vehicle

This article studies the power management control strategy of electric drive system and, in particular, improves the fuel economy for electric drive tracked vehicles. Combined with theoretical analysis and experimental data, real-time control oriented models of electric drive system are established. Taking into account the workloads of engine and the SOC (state of charge) of battery, a fuzzy logic based power management control strategy is proposed. In order to achieve a further improvement in fuel economic, a DEHPSO algorithm (differential evolution based hybrid particle swarm optimization) is adopted to optimize the membership functions of fuzzy controller. Finally, to verify the validity of control strategy, a HILS (hardware-in-the-loop simulation) platform is built based on dSPACE and related experiments are carried out. The results indicate that the proposed strategy obtained good effects on power management, which achieves high working efficiency and power output capacity. Optimized by DEHPSO algorithm, fuel consumption of the system is decreased by 4.88% and the fuel economy is obviously improved, which will offer an effective way to improve integrated performance of electric drive tracked vehicles.

Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system

The fuel economy performance of plug-in hybrid electric vehicles (PHEVs) strongly depends on the power management strategy. This study proposes an integrated power management for a PHEV with multiple energy sources, including a semi-active hybrid energy storage system (HESS) and an assistance power unit (APU). The HESS consists of battery packs and ultracapacitor packs. In the integrated control strategy, the output power between the battery packs and ultracapacitor packs is regulated by the model predictive control strategy, while the output power between the APU and HESS is allocated by the rule-based strategy. In the model predictive control process, a period of the future velocity will be predicted, and the dynamic programming algorithm will be applied to optimize the control strategy accordingly. The robustness of the proposed approach is verified by three typical driving cycles, including the Manhattan cycle, CBDC cycle and UDDSHDV cycle. The results show that the proposed control strategy can promote fuel economy compared with the original control strategy, especially in the charge sustain mode under the MANHATTAN driving cycle (21.88% improvement).

An optimised FOPID controller for dynamic voltage stability and reactive power management in a stand-alone micro grid

This paper proposes the application of fractional order PID controller (FOPID) for reactive power compensation and stability analysis in a stand-alone micro grid. For enhancement of voltage stability and reactive compensation of the isolated system, a SVC based controller has been incorporated. This paper emphasizes the role of fractional PID based SVC controller for reactive power management and improved stability in the stand alone micro grid, as it provides a special advantage of having two more degree of freedom for accurate tuning in comparison with the conventional controller The system performance, particularly the variations in different parameters values are studied properly with different input parameters and loading conditions. Further improvement of stability margin and optimisation of the system parameters have been achieved by the controller, based on Imperialist competitive algorithm.

고정익 수직이착륙 무인항공기를 위한 하이브리드-전기 추진시스템의 타당성 연구

General VTOL aircraft uses gas turbine engine which has high power to weight ratio. However, in the VTOL UAV in small sector, the gas turbine as a prime mover is not adequate because of the limitation of the high fuel consumption ratio of the gas turbine. In this research, The Series Hybrid-Electric Propulsion System(SHEPS) has been proposed and technology survey & comparison analysis has conducted to constitute propulsion system for engine, electric motor and battery. To achieve this object a 65kg-class P-UAV from “Company I” was used. And to estimate the validity of power control algorithm and developed power management control, Matlab/simulink ® has been used for the simulation. As a result, the developed algorithm worked comparatively well and the research has predicted that SHEPS was satisfied enough for 7 hour of endurance for mission profile.

Dynamic modeling and power management for stand-alone AC-coupled photovoltaic system

There are many remote areas in the world where people are living without electricity and expanding the public grids to them are uneconomical. For this reason, this paper studies a dynamic behavior of the power flow in a stand-alone AC-coupled photovoltaic (PV) system to optimize the size of the system components based on the weather conditions of the selected site and the load power demands. This system consists of a PV generator, a battery storage system (BSS), a PV inverter, a battery inverter/charger, and an AC load power. The PV generator is intermittent source of energy, so this system has to be combined with a BSS to organize the energy between the PV generator and the load. The power management uses the fuzzy power management controller to optimize the system performance by distributing the power inside the system and by managing the charge and discharge of power flow for the BSS. Real-time meteorological data of the site under study and practical load profile of a small household are used as inputs for the proposed management system. The results of the proposed system showed a good performance under various operating conditions, and maintained the state of charge of BSS at a reasonable level. In addition, the results gave a clear visualization of the behavior of the switched-mode power supply to regulate the power flow between the sources and the load on one side and to match between all components of the system on the other side. The main contributions of this work are the ability to give a mathematical model of the all components of the system and the control management which determine an optimal design of the system at any site.

Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach

Shipboard integrated power systems, the key enablers of ship electrification, call for effective power management control (PMC) to achieve optimal and reliable operation in dynamic environments under hardware limitations and operational constraints. The design of PMC can be treated naturally in a model predictive control (MPC) framework, where a cost function is minimized over a prediction horizon subject to constraints. The real-time implementation of MPC-based PMC, however, is challenging due to computational complexity of the numerical optimization. In this paper, an MPC-based PMC for a shipboard power system is developed and its real-time implementation is investigated. To meet the requirements for real-time computation, an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm is applied to solve a constrained MPC optimization problem. Several operational scenarios are considered to evaluate the performance of the proposed PMC solution. Simulations and experiments show that real-time optimization, constraint enforcement, and fast load following can be achieved with the IPA-SQP algorithm. Different performance attributes and their tradeoffs can be coordinated through proper tuning of the design parameters.

An integrated Control Approach and Power Management of Stand-alone Hybrid Wind/PV/Battery Power Generation System with Maximum Power Extraction Capability

The production of electricity from renewable energy sources likewind and photovoltaic energy has increased in recent years, due to en -vironmental problems and the shortage of traditional energy sources.In this article we present a detailed mathematical model and a controlscheme for hybrid wind and PV based DG system with battery and max-imum power extraction capability for isolated mode of operation. Thewind power generation system uses wind turbine (WT), a permanentmagnet synchronous generator (PMSG), a three-phase diode rectifierbridge, DC/DC boost converter with maximum power point tracking(MPPT) controller. The PV generation system uses PV array, a boostconverter with maximum power point tracking controller. Both sourcesand battery are connected to common dc bus with a dc link capacitorand supply power to load through PWM voltage source inverter. Theoverall control system consists of MPPT controller for both Wind andPV power system, a bi-directional dc-dc converter controller for batteryenergy storage management and load side inverter controller for volt -age and frequency regulation. Control strategies for individual systemcomponents of the proposed system are designed with a view to achievean acceptable level of voltage and frequency regulation while extract -ing the maximum power from wind and PV system. The performanceof the developed hybrid system is investigated in terms of voltage andfrequency regulation capability under changing wind, solar irradiation and variable load conditions.