Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Model and Algorithm for the Optimal Controlled Partitioning of Power Systems

解列是电力系统在故障时结构完整性得不到保证的情况下分裂为2个或多个孤立稳定运行子系统的过程或操作。及时恰当的主动解列操作将能有效地避免因保护连锁动作使得电力系统被动解列而崩溃所造成的巨额经济损失。结合图论中的相关概念和输电网最优解列的物理过程，建立电力系统最优主动解列断面选择问题的完整数学模型，并基于含连通图约束的背包问题（connected graph constrained knapsack problem，CGKP）及其求解算法提出“搜索＋调整”的2阶段求解方法。为降低问题的复杂性，将完整数学模型分解成图的最优平衡分割问题和基于优化潮流的调节问题，并分2个阶段分别求解。其中，图的最优平衡分割问题又被分解成多个CGKP，利用基于含CGKP的图分割方法进行求解。所提出的主动解列策略具有较强理论基础，计算复杂度低，算例的计算结果证明了该模型及算法的正确性和有效性。

Optimal coordinated voltage control of power systems

An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the parameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattem-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.

Optimal Power Flow Using Firefly Algorithm with Unified Power Flow Controller

Firefly algorithm is the new intelligent algorithm used for all complex engineering optimization problems. Power system has many complex optimization problems one of which is the optimal power flow (OPF). Basically, it is minimizing optimization problem and subjected to many complex objective functions and constraints. Hence, firefly algorithm is used to solve OPF in this paper. The aim of the firefly is to optimize the control variables, namely generated real power, voltage magnitude and tap setting of transformers. Flexible AC Transmission system (FACTS) devices may used in the power system to improve the quality of the power supply and to reduce the cost of the generation. FACTS devices are classified into series, shunt, shunt-series and series-series connected devices. Unified power flow controller (UPFC) is shunt-series type device that posses all capabilities to control real, reactive powers, voltage and reactance of the connected line in the power system. Hence, UPFC is included in the considered IEEE 30 bus for the OPF solution.

Research on Comprehensive Control of Power Quality of Port Distribution Network Considering Large-Scale Access of Shore Power Load

In view of the problem of power quality degradation of port distribution network after the large-scale application of shore power load,a method of power quality management of port distribution network is proposed.Based on the objective function of the best power quality management effect and the smallest investment cost of the management device,the optimization model of power quality management in the distribution network after the large-scale application of large-capacity shore power is constructed.Based on the balance between the economic demand of distribution network resources optimization and power quality management capability,the power quality of distribution network is considered comprehensively.The proposed optimization algorithm for power quality management based on Matlab and OpenDSS is proposed and analyzed for port distribution networks.The simulation results show that the proposed optimizationmethod can maximize the power qualitymanagement capability of the port distribution network,and the proposed optimization algorithm has good convergence and global optimization finding capability.

A novel fractional uplink power control framework for self-organizing networks

Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).

Wind Power Flow Optimization and Control System Based on Rapid Energy Storage

风电功率的间歇与波动致使电场容量可信度低、可调度性差；同时易引起局部电网的电压不稳、频率波动，影响了系统的电能质量及稳定性。针对此现象，将超级电容器与蓄电池组成快速储能装置，用于风电的潮流优化控制。采用三重双向直流变换电路控制储能元件间的功率流动；采用四象限交直流变换电路控制储能与电网间的能量交换。提出基于超级电容器电压低频波动抑制的功率分配方法，可显著减少蓄电池的充放次数；提出基于储能元件荷电状态的储能能量调整规则，可避免储能元件的过充和频繁深度放电，以优化其功率调节能力。实验结果表明，系统可实现2种储能元件的优势互补，能有效平滑调节风电注入电网的有功功率，并实时补偿控制风电接入点的无功功率。

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

The integration of wind turbines(WTs)in variable speed drive systems belongs to the main factors causing lowstability in electrical networks.Therefore,in order to avoid this issue,WTs hybridization with a storage system is a mandatory.This paper investigates WT system operating at variable speed.The system contains of a permanent magnet synchronous generator(PMSG)supported by a battery storage system(BSS).To enhance the quality of active and reactive power injected into the network,direct power control(DPC)scheme utilizing space-vector modulation(SVM)technique based on proportional-integral(PI)control is proposed.Meanwhile,to improve the rendition of this method(DPC-SVM-PI),the rooted tree optimization technique(RTO)algorithm-based controller parameter identification is used to achieve PI optimal gains.To compare the performance ofRTO-based controllers,they were implemented and tested along with some other popular controllers under different working conditions.The obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion(THD),overshoot percentage,settling time,rise time,average active power value,overall efficiency,and active power steadystate error.

Wind Farm Coordinated Control for Power Optimization

以降低风电场尾流损失、优化风场出力为目标,设计基于Laguerre函数非线性预测控制（nonlinear modelpredictive control,NLMPC）方案的风场集群控制器。该控制器应用风场动态尾流模型,通过NLMPC统一调整风场内各机组转速以提升风场功率。在控制器设计中,使用有效风速预测误差校正对预测模型失配及超短期风速预测误差进行补偿,引入Laguerre函数降低滚动时域优化计算负担并分析了控制器对风速预测误差的鲁棒性能。仿真研究表明,集群控制器能够在不同风速条件下提升风场功率、降低优化计算负担,且对风速预测模型失配与风场自然风速预测误差具有鲁棒性。

Steady-state Voltage Security-constrained Optimal Frequency Control for Weak HVDC Sending-end AC Power Systems

Due to the fact that a high share of renewable energy sources(RESs)are connected to high-voltage direct current(HVDC)sending-end AC power systems,the voltage and frequency regulation capabilities of HVDC sending-end AC power systems have diminished.This has resulted in potential system operating problems such as overvoltage and overfrequency,which occur simultaneously when block faults exist in the HVDC link.In this study,a steady-state voltage security-constrained optimal frequency control method for weak HVDC sending-end AC power systems is proposed.The integrated virtual inertia control of RESs is employed for system frequency regulation.Additional dynamic reactive power compensation devices are utilized to control the voltage of all nodes meet voltage security constraints.Then,an optimization model that simultaneously considers the frequency and steady-state voltage security constraints for weak HVDC sending-end AC power systems is established.The optimal control scheme with the minimum total cost of generation tripping and additional dynamic reactive power compensation required is obtained through the optimization solution.Simulations are conducted on a modified IEEE 9-bus test system and practical Qing-Yu line commutated converter based HVDC(LCC-HVDC)sending-end AC power system to verify the effectiveness of the proposed method.

Increasing realism in modelling energy losses in railway vehicles and their impact to energy-efficient train control

The reduction of energy consumption is an increasingly important topic of the railway system.Energy-efficient train control(EETC)is one solution,which refers to mathematically computing when to accelerate,which cruising speed to hold,how long one should coast over a suitable space,and when to brake.Most approaches in literature and industry greatly simplify a lot of nonlinear effects,such that they ignore mostly the losses due to energy conversion in traction components and auxiliaries.To fill this research gap,a series of increasingly detailed nonlinear losses is described and modelled.We categorize an increasing detail in this representation as four levels.We study the impact of those levels of detail on the energy optimal speed trajectory.To do this,a standard approach based on dynamic programming is used,given constraints on total travel time.This evaluation of multiple test cases highlights the influence of the dynamic losses and the power consumption of auxiliary components on railway trajectories,also compared to multiple benchmarks.The results show how the losses can make up 50%of the total energy consumption for an exemplary trip.Ignoring them would though result in consistent but limited errors in the optimal trajectory.Overall,more complex trajectories can result in less energy consumption when including the complexity of nonlinear losses than when a simpler model is considered.Those effects are stronger when the trajectory includes many acceleration and braking phases.

Damping Controller Based Quantum Particle Swarm Optimization for VSC HVDC to Improve Power System Stability

The use of the supplementary controllers of a High Voltage Direct Current (HVDC) based on Voltage Source Converter (VSC) to damp low Frequency oscillations in a weakly connected system is surveyed. Also, singular value decomposition (SVD)-based approach is used to analyze and assess the controllability of the poorly damped electromechanical modes by VSC-HVDC different control channels. The problem of supplementary damping controller based VSC-HVDC system is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO). Individual designs of the HVDC controllers using QPSO method are evaluated. The effectiveness of the proposed controllers on damping low frequency oscillations is checked through eigenvalue analysis and non-linear time simulation under various disturbance conditions over a wide range of loading.

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

Ant Lion Optimization Approach for Load Frequency Control of Multi-Area Interconnected Power Systems

This work proposes a novel nature-inspired algorithm called Ant Lion Optimizer (ALO). The ALO algorithm mimics the search mechanism of antlions in nature. A time domain based objective function is established to tune the parameters of the PI controller based LFC, which is solved by the proposed ALO algorithm to reach the most convenient solutions. A three-area interconnected power system is investigated as a test system under various loading conditions to confirm the effectiveness of the suggested algorithm. Simulation results are given to show the enhanced performance of the developed ALO algorithm based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Bat Algorithm (BAT) and conventional PI controller. These results represent that the proposed BAT algorithm tuned PI controller offers better performance over other soft computing algorithms in conditions of settling times and several performance indices.

Adaptive and distance-driven power control scheme in mobile ad hoc networks

In order to save the energy and reduce the latency of the end-to-end transmission in mobile ad hoc networks an adaptive and distance-driven power control ADPC scheme is proposed by means of distance research in random geometrics. Through mathematical proof the optimal number of relay nodes and the optimal location of each node for data transmission can be obtained when a distance is given.In the ADPC first the source node computes the optimal number and the sites of the relay nodes between the source and the destination nodes.Then it searches feasible relay nodes around the optimal virtual relay-sites and selects one link with the minimal total transmission energy consumption for data transmission.Simulation results show that the ADPC can reduce both the energy dissipation and the end-to-end latency of the transmission.

Tube Model Predictive Control Based Cyber-attack-resilient Optimal Voltage Control Strategy in Wind Farms

Optimal voltage controls have been widely applied in wind farms to maintain voltage stability of power grids.In order to achieve optimal voltage operation,authentic grid information is widely needed in the sensing and actuating processes.However,this may induce system vulnerable to malicious cyber-attacks.To this end,a tube model predictive control-based cyber-attack-resilient optimal voltage control method is proposed to achieve voltage stability against malicious cyber-attacks.The proposed method consists of two cascaded model predictive controllers(MPC),which outperform other peer control methods in effective alleviation of adverse effects from cyber-attacks on actuators and sensors of the system.Finally,efficiency of the proposed method is evaluated in sensor and actuator cyber-attack cases based on a modified IEEE 14 buses system and IEEE 118 buses system.Index Terms-Attack-resilient control,optimal voltage control,tube-based model predictive control,wind farm-connected power system.

Learning the optimal power flow:Environment design matters

To solve the optimal power flow(OPF)problem,reinforcement learning(RL)emerges as a promising new approach.However,the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment.In this work,we collect and implement diverse environment design decisions from the literature regarding training data,observation space,episode definition,and reward function choice.In an experimental analysis,we show the significant impact of these environment design options on RL-OPF training performance.Further,we derive some first recommendations regarding the choice of these design decisions.The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

Optimal Energy-Efficient Transmission for Hybrid Spectrum Sharing in Cooperative Cognitive Radio Networks

In order to improve the energy efficiency(EE)in cognitive radio(CR),this paper investigates the joint design of cooperative spectrum sensing time and the power control optimization problem for the secondary user systems to achieve the maximum energy efficiency in a cognitive network based on hybrid spectrum sharing,meanwhile considering the maximum transmit power,user quality of service(QoS)requirements,interference limitations,and primary user protection.The optimization of energy efficient sensing time and power allocation is formulated as a non-convex optimization problem.The Dinkelbach’s method is adopted to solve this problem and to transform the non-convex optimization problem in fractional form into an equivalent optimization problem in the form of subtraction.Then,an iterative power allocation algorithm is proposed to solve the optimization problem.The simulation results show the effectiveness of the proposed algorithms for energy-efficient resource allocation in the cognitive network.

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.

Adaptive Power Control for mutual Interference Avoidance in Industrial Internet-of-Things

With the vigorous development of the Internet of Things and 5G technology, such as machine-to-machine and device-todevice, all kinds of data transmission including environmental monitoring and equipment control strengthens the key role of wireless sensor networks in the large-scale wireless communication system. However, especially in the complex industrial wireless applications, the low utilization efficiency of the limited wireless radio resource enhances the coexistence problem between heterogeneous networks. In this paper, from the severe mutual interference point of view, a mathematical model regarding cumulative interferences in the industrial wireless sensor networks is described. Then, from the perspective of mutual interference avoidance, an adaptive power control scheme is proposed in order to handle the normal communication needs on both the primary link and the secondary link. At last, nonlinear programming is taken to solve the corresponding optimization problem. Some typical analyses are given to verify the effectiveness of the proposed scheme on optimizing the tradeoff between the system throughput and energy consumption. Especially, the energy-efficiency of the novel scheme for Industrial Internet of Things is also analysed. Results show that the proposed power control is efficient. The throughput could be enhanced and the energy consumption could be reduced with the guarantee of mutual interference avoidance.