Research on System Control and Energy Management Strategy of Flux-Modulated Compound-Structure Permanent Magnet Synchronous Machine

The flux-modulated compound-structure permanent magnet synchronous machine (CS-PMSM), composed of a brushless double rotor machine (DRM) and a conventional permanent magnet synchronous machine (PMSM), is a power split device for plug-in hybrid electric vehicles. In this paper, its operating principle and mathematical model are introduced. A modified current controller with decoupled state feedback is proposed and verified. The system control strategy is simulated in Matlab, and the feasibility of the control system is proven. To improve fuel economy, an energy management strategy based on fuzzy logic controller is proposed and evaluated by the Urban Dynamometer Driving Schedule (UDDS) drive cycle. The results show that the total energy consumption is similar to that of Prius 2012.

Analysis of Quality Control and Safety Management in Electrical Engineering

In the development process of the society nowadays,electrical engineering has been widely used with remarkable results,but there are still some problems in the course of actual management,which requires effective technical measures to control the quality in electrical engineering and the enhancement of safety management work.Therefore,in order to better ensure safety management requirements in electrical engineering and strengthen related trainings and thus better carried out safety management and quality control to meet respective requirements in the real world,in this paper,a brief analysis is conducted in quality control and safety management in electrical engineering.

Application of Intelligent Power Consumption Management and Control Technology in Enterprise Off-site Enforcement

Regarding the current difficulties in SME(small and medium enterprise)supervision,especially considering the problem that the emission reduction task cannot be effectively implemented under the off-site daily supervision and the new normal of epidemic prevention with control of heavily polluted weather,a new type of intelligent power management technology has been proposed.Power information collection equipment and intelligent data collection and transmission terminal are installed to collect power consumption information of enterprise production facilities and pollution control facilities,and their operating conditions are monitored in 24 h.Abnormal operation alarm and closed-loop disposal management are provided through the software platform.The practical application of power monitoring technology in Rizhao City proves that the system alarms accurately and monitors efficiently.On the one hand,it has improved the efficiency of daily supervision of the ecological environment department,and can accurately investigate and correct corporate pollution control violations.On the other hand,it has enriched environmental supervision methods to accurately control the implementation of emergency emission reduction measures.The application of this technology has realized the transformation from civil defense to technical defense,from random law enforcement to precise law enforcement,and from on-site law enforcement to off-site law enforcement in the supervision of polluting enterprises in the jurisdiction,creating a new mode of off-site law enforcement for enterprises.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Supervisory control of the hybrid off-highway vehicle for fuel economy improvement using predictive double Q-learning with backup models

This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.

The Application of Principle of Automatic Control in the site Management of Electric Power Construction Enterprise

It is need to establish site project department during the construction of electric power project.The head office should design and build the main structure,continually improve structure of project department,meantime,positively apply principle of automatic control to improve the site management of electric power construction.In this article,the application strategy of principle of automatic control is analyzed focusing on the site work of electric power construction.

含电动汽车和光伏的配电网演化动态博弈调度策略

针对大规模电动汽车的无序充电行为可能导致电网出现的三相不平衡问题,基于演化动态博弈模型,提出一种考虑三相负荷平衡和光伏消纳的电动汽车充电调度策略。首先,基于电动汽车接入时段的主观性以及光伏出力和负荷的不确定性,引入游牧集群和定居集群之间的演化动态博弈方程;然后,考虑用户侧和电网侧双方利益关系,设计以最小化电动汽车充电费用、三相负荷不平衡度和无功需求为目标的调度优化模型;最后,通过对某商业区配电网算例进行仿真求解,对比分析三相有功曲线、三相无功曲线以及荷电状态曲线的变化趋势。算例结果表明,该调度策略可有效降低三相不平衡度,满足无功补偿的需求以及改善用户充电成本。

基于DBO-MPC的混合动力汽车能量管理策略

混合动力汽车(hybrid electrical vehicle,HEV)的能量管理策略直接决定了车辆的燃油经济性、驾驶性能和寿命,为解决HEV能量管理策略的最优性与实时行驶工况不确定性之间的矛盾,以混联式HEV为研究对象,提出一种基于模型预测控制(model predictive control,MPC)与蜣螂优化算法(dung beetle optimizer,DBO)的HEV能量管理策略。首先,该策略采用基于堆叠式长短时记忆神经网络(stacked long-short term memory neural network,Stacked LSTM-NN)的车速预测模型预测未来行驶车速。其次,根据预测车速将混合动力汽车的功率分配问题描述为MPC预测范围内的滚动优化问题,提出考虑燃料消耗和电池保护的成本函数,利用DBO算法对预测时域内发动机功率进行优化求解。最后,在城市道路循环(urban dynamometer driving schedule,UDDS)工况下分别对所提策略的车速预测精度和经济性与其他策略进行仿真对比验证。结果表明:与传统LSTM速度预测模型相比,Stacked LSTM速度预测模型的RMSE降低了13.9%,每步平均预测时间减少1 ms;与基于规则的策略相比,基于DBO-MPC的策略模型节油率达到25.3%,同时SOC状态波动更为平稳,对电池的保护效果更好。

混合动力拖拉机能量管理策略综述

对混合动力拖拉机进行研究,可有效提高燃料经济性,而能量管理策略的研究则是提高混合动力拖拉机燃油经济性的核心技术问题。通过文献整理及文献翻译,对能量管理的内涵进行分析。从基于规则和基于优化两个角度,对几种常见的能量管理策略进行分类。根据不同种类混合动力拖拉机的构型,分析其所适合的能量管理策略及其特点,并对之后能量管理控制策略应用于拖拉机上的研究方向做出了建议。

电动汽车动力电池热管理系统设计

针对电动汽车各系统温度调控体系,设计一套电动汽车动力电池热管理系统,包含电机冷却回路、乘员舱热管理回路以及电池热管理回路。在计算系统热量、确定电池温度采用逻辑门限值控制、乘员舱温度采用模糊控制的基础上,利用Comsol和Simulink分别建立动力电池模型和乘员舱及控制系统模型,利用二者进行联合仿真。结果表明:基于标准城市路况条件及大连气候,模拟环境温度35℃下,系统运行3276s时,电池单体最高温度可由40.00℃降至29.05℃,且乘员舱温度维持在25℃左右;模拟环境温度-10℃下,系统运行3276s时,电池单体最低温度可由-10.00℃升至23.19℃,且乘员舱温度满足模糊控制器设定的控制需求。

增程式电动物流车队列的能量管理策略研究

以增程式电动物流车队列为研究对象,为提高整个队列的燃油经济性,从队列的协同自适应巡航控制和能量管理策略两方面进行研究。利用车对车通信和前车领航车跟随式通信拓扑结构,基于分布式模型预测控制,设计了以稳定性、舒适性和经济性为优化目标的协同自适应巡航控制器。将增程式电动物流车队列的能量管理策略描述为一个完全合作类型的多智能体强化学习问题,所有智能体共同探索在不同车辆状态下的最优控制动作,提出了基于多智能体强化学习的能量管理策略。仿真结果表明,所设计的生态协同自适应巡航控制策略能够有效地平衡车辆队列的稳定性和经济性。以动态规划为基准,与单智能体算法相比,基于多智能体深度确定性策略梯度算法的能量管理策略可以在显著提高学习速率的同时获得近似最优解。

基于模糊控制的燃料电池电动轻卡能量管理策略研究

以某款燃料电池电动轻型卡车作为研究对象,以提高燃料电池系统的经济性和耐久性为目的,利用Matlab/Simulink搭建了燃料电池电动轻卡动力系统仿真模型,对其能量管理策略进行优化。在原模糊控制策略的基础上进行改进,对燃料电池输出功率变化率进行约束,制定了一种改进后的模糊控制策略。将改进后的模糊控制策略与有限状态机控制策略和原模糊控制策略进行对比仿真验证。结果表明,在NEDC和UDDS循环工况下,改进后的模糊控制策略比原模糊控制策略氢耗量分别减少5.65%和8.29%;与有限状态机控制策略相比,改进后的模糊控制策略的氢耗量分别减少16.63%和10.64%,并且改进后的模糊控制策略的燃料电池输出功率波动幅度更小,变化更加稳定,提高了燃料电池系统的经济性和耐久性。

地铁综合监控系统数据统分平台应用研究

随着地铁行业数字化的飞速发展,逐渐增加的海量数据使原有简单呈现方式无法满足运营管理需求。广州地铁18号、22号线采用综合监控系统数据统分平台进行数据管理,结合地铁业务需求,根据运营场景布局,凭借综合监控系统数据统分平台的大数据处理及运算能力,按照客流、行车、能耗、环控及电力设备5个维度,适用于行业统计分析的各项指标可视化展示。平台的应用可提高地铁数据体系建设中数据管理的基础能力,为制订更科学合理的管理方法提供决策依据,从而全面提升管理和决策能力,不断促进地铁全面信息化建设、数字化转型,加速智慧地铁建设。

电动拖拉机混合电源负载自适应能量管理策略研究

针对纯电动拖拉机工况随机性大、动态负荷变化频繁导致能量利用率低等问题,提出了基于负载自适应的蓄电池/超级电容混合电源的纯电动拖拉机动力电源供能系统。通过分析纯电动拖拉机的作业工况特点以及功率需求,采用动态规划分析方法找出动力系统最优控制模式,并且从动态规划分析结果中提出分段式控制规则,建立了功率划分参数模型与负载统计量之间的函数关系。在此基础上提出了一种基于负载自适应规则的电动拖拉机能量管理策略,并对平稳路况组合负载周期和非平稳路况随机负载周期模式进行了验证实验。实验结果表明,与普通控制规则的能量管理策略相比,本文所提出的电动拖拉机负载自适应能量管理策略具有更强的电池保护和节能能力,能实现负载自适应模式下的最优能量管理,其中在不同工况下电池安培小时吞吐量及电源系统总能量损失分别降低10.5%~15.7%和8.7%~15.1%,表明所提出的电动拖拉机负载自适应能量管理策略能够以较低的计算成本实时在线实现最优的能量管理。

面向新型电力负荷管理的混杂负荷集群协同管控

针对新型电力负荷管理中负荷协同控制问题,提出了面向新型电力负荷管理的混杂集群协同管控方法。以空调和电热水器混杂负荷集群为控制对象,分析了空调和电热水器负荷的物理和响应特性,分别建立了适用于空调和电热水器的聚合控制模型。基于空调和电热水器负荷的聚合响应特性,利用电热水器负荷和空调负荷响应特性上的优势和互补特性,构建了空调集群和电热水器集群的协同管控模型,控制大量负荷输出稳定响应。最后,设置不同的清洁能源发电场景,基于上述算法对清洁能源出力进行跟踪仿真,结果表明所提负荷集群协同算法在不同场景下的跟踪误差都控制在±0.04%之内,验证了该算法的有效性。

基于需求功率预测的电动拖拉机能量管理策略

针对电动拖拉机在犁耕工况下电机需求电流波动比较大的特点,为了改善动力电池的输出电流过高或过低及电动拖拉机犁耕持续作业时间短的现象,利用超级电容高功率密度的特点,设计了一种锂电池+超级电容结构的双电源电动拖拉机,并建立了Amesim/Simulink联合仿真模型。以模型预测控制作为双电源系统的能量管理方法,基于长短期记忆神经网络建立电动拖拉机犁耕工况下的需求功率预测模型,使用动态规划算法求解最佳的锂电池输出电流。仿真结果表明:相比于模糊控制策略,基于模型预测控制策略有效降低了锂电池大电流放电的频率且峰值电流降低了40%,有效提高了锂电池的使用寿命;超级电容的SOC保持在比较高的范围内,且电动拖拉机在犁耕工况下的单位里程能量消耗降低了2.17%,实现了双电源电流分配最优,提高了电动拖拉机的动力性和经济性。

综采机电设备智能化管控平台研究与应用

针对传统综采机电设备管理模式过度依赖人工方式存在的管理效率低下、反应迟缓、管理粒度较粗以及难以实现对综采机电设备的精细化管理与调度等问题,北京天玛智控科技股份有限公司运用物联网、大数据和人工智能等先进技术,构建了综采机电设备智能化管控平台,以实现对关键装备的状态监测、故障预测、寿命评估与精细化管理。介绍了综采机电设备智能化设备管控平台总体架构、数据采集、动态监测和故障预测以及智能调度与优化等关键技术,相关技术成果在国能神东煤炭有限责任公司上湾、榆家梁、保德等煤矿进行了应用。应用结果表明,该管控平台能够极大提高设备管理的智能化和科学决策水平,对企业安全生产与降本增效具有重要作用。

基于级联式模糊控制的电氢耦合直流微网能量管理策略研究

针对电氢混合储能系统在平抑直流微网中功率波动时面临的功率分配问题,提出了一种基于级联式模糊控制的电氢耦合直流微网能量管理策略。该策略中一次模糊控制器依据储氢罐储氢状态(stateofhydrogenstorage,SOH)与锂电池荷电状态(state of charge, SOC)求解出一次功率分配因子,对直流微网净功率进行一次分配;二次模糊控制器结合一次功率分配参考值与SOH对一次功率分配因子作出校正。此外,为使氢储能系统中具有非线性工作特性的电流控制型装置(电解槽、燃料电池)能够对能量管理系统作出高效响应,采用插值法将功率分配参考值转换为电流参考值。通过Matlab/Simulink仿真结果证明,所提能量管理策略有效缩小了氢储能系统在非合理区间的功率波动范围并提高了氢储能系统中装置的响应精度与速度。