Operation Control Method of Relay Protection in Flexible DC Distribution Network Compatible with Distributed Power Supply

A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to poor performance.The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section.Then,an operation control method for relay protection based on adaptive weight and whale optimization algorithm(WOA)is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current.The adaptive weight and WOA are employed to obtain the optimal strategy for relay protection operation control,reducing the action time and impulse current.Experimental results demonstrate the effectiveness of the proposed method in accurately locating faults and improving relay protection performance.The longest operation time is reduced by 4.7023 s,and the maximum impulse current is limited to 0.3 A,effectively controlling the impact of large impulse currents and enhancing control efficiency.

Special Issue on "Recent Advances in Power System Control" for International Journal of Control,Automation,and Systems

Today the high quality power supply is of essential in the economic development in a country. With the development of modem power systems and increasing demand for power supply, the electric power industry is facing a great challenge in meeting the increased load demand with highest reliability and security with minimum transmission expenditure.

Machine Learning and Data-Driven Techniques for the Control of Smart Power Generation Systems:An Uncertainty Handling Perspective

Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.

Reliability and sensitivity analysis of loop-designed security and stability control system in interconnected power systems

Security and stability control system(SSCS)in power systems involves collecting information and sending the decision from/to control stations at different layers;the tree structure of the SSCS requires more levels.Failure of a station or channel can cause all the execution stations(EXs)to be out of control.The randomness of the controllable capacity of the EXs increases the difficulty of the reliability evaluation of the SSCS.In this study,the loop designed SSCS and reliability analysis are examined for the interconnected systems.The uncertainty analysis of the controllable capacity based on the evidence theory for the SSCS is proposed.The bidirectional and loop channels are introduced to reduce the layers and stations of the existing SSCS with tree configuration.The reliability evaluation and sensitivity analysis are proposed to quantify the controllability and vulnerable components for the SSCS in different configurations.By aiming at the randomness of the controllable capacity of the EXs,the uncertainty analysis of the controllable capacity of the SSCS based on the evidence theory is proposed to quantify the probability of the SSCS for balancing the active power deficiency of the grid.

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。

Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Design and implementation of power and phase feedback control system for ICRH on EAST

Ion cyclotron wave resonance heating(ICRH) is one of the most important auxiliary methods to heat plasma in the Experimental Advanced Superconducting Tokamak(EAST). Several megawatts of power is transmitted through separate coaxial lines and coupled with the plasma through arrays of loop antennas. The parameters of the ICRH system, including the injected power and phasing between antenna straps, are critical to the coupling efficiency of the power as well as the resulting impact on the heating efficiency. In this paper, we present a system for feedback control of the phase between the current straps and the ICRH power on EAST. The feedback control system was tested using both a matched dummy load and a plasma load, and it successfully maintained stable operation in the 2016 EAST campaign. Good control of the injected power and wave phases was achieved during edgelocalized mode operation.

Fixed time integral sliding mode controller and its application to the suppression of chaotic oscillation in power system

Chattering phenomenon and singularity are still the main problems that hinder the practical application of sliding mode control. In this paper, a fixed time integral sliding mode controller is designed based on fixed time stability theory,which ensures precise convergence of the state variables of controlled system, and overcomes the drawback of convergence time growing unboundedly as the initial value increases in finite time controller. It makes the controlled system converge to the control objective within a fixed time bounded by a constant as the initial value grows, and convergence time can be changed by adjusting parameters of controllers properly. Compared with other fixed time controllers, the fixed time integral sliding mode controller proposed in this paper achieves chattering-free control, and integral expression is used to avoid singularity generated by derivation. Finally, the controller is used to stabilize four-order chaotic power system. The results demonstrate that the controller realizes the non-singular chattering-free control of chaotic oscillation in the power system and guarantees the fixed time convergence of state variables, which shows its higher superiority than other finite time controllers.

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.

An Advanced FMRL Controller for FACTS Devices to Enhance Dynamic Performance of Power Systems

The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system (FACTS) in this paper is an advanced super-conducting magnetic energy storage (ASMES). Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller (FMRLC) is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters (i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system (i.e., single machine-infinite bus (SMIB) system), under various fault conditions and disturbances.

Design of a Fixed-Order Robust Controller to Damp Inter-Area Oscillations in Power Systems

This paper presents the design of a robust fixed-order H∞ controller to damp out the inter-area oscillations and to enhance the stability of the power system. The proposed H∞ approach is based on shaping the open-loop transfer function in the Nyquist diagram through minimizing the quadratic error between the actual and the desired open loop transfer functions in the frequency domain under linear constraints that guarantee robustness and stability. The proposed approach is robust with respect to multi-model uncertainty closed-loop sensitivity functions in the Nyquist diagram through the constraints on their infinity norm. The H∞ constraints are linearized with the help of a desired open-loop transfer function. The controller is designed using the convex optimization techniques in which the difference between the open-loop transfer function and the desired one is minimized. The two-area four-machine test system is selected to evaluate the performance of the designed controller under different load conditions as well as different levels of wind penetrations.

Simulation of Power Control and Diversity of Cellular CDMA

ＳｉｍｕｌａｔｉｏｎｏｆＰｏｗｅｒＣｏｎｔｒｏｌａｎｄＤｉｖｅｒｓｉｔｙｏｆＣｅｌｌｕｌａｒＣＤＭＡＲｏｍａｎＰｉｃｈｎａ；ＱｉａｎｇＷａｎｇ；ＶｉｊａｙＫ．Ｂｈａｒｇａｖａ；ＪｉａｌｉｎＺｏｕ；ＲｏｎＫｅｒｒ（ＵｎｉｖｅｒｓｉｔｙｏｆＶｉｃｔｏｒｉ...

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 pa- rameters 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-pattern-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.

Charging System of ECRH High-Voltage Power Supply and its Control System

High-voltage power supply (HVPS) of Electron Cyclotron Resonance Heating(ECRH) for HT-7 and HT-7U is presently being constructed. The high voltage (100 kV) en-ergy of HVPS is stored in the capacitor banks, and they can power one or two gyrotrons. All theoperation of the charging system will be done by the control system, where the field signals areinterfaced to programmable logic controller (PLC). The use of PLC not only simplifies the controlsystem, but also enhances the reliability. The software written by using configuration softwareinstalled in the master computer allows for remote and multiple operator control, and the statusand data information is also remotely available.

Coordinated nonlinear robust control of TCSC and excitation for multi-machine systems

An advanced nonlinear robust control scheme is proposed for multi-machine power systems equipped with thyristor-controlled series compensation (TCSC). First, a decentralized nonlinear robust control approach based on the feedback linearization and H∞ theory is introduced to eliminate the nonlinearities and interconnections of the studied system, and to attenuate the exogenous disturbances that enter the system. Then, a system model uilt up, which has considered all the generators' and TCSC's dynamics, and the effects of uncertainties such as disturbances. Next, a decentralized nonlinear robust coordinated control law is developed based on this model. Simulation results on a six-machine power system show that the transient stability of the power system is obviously improved and the power transfer capacity of long distance transmission lines is enhanced regardless of fault locahons and system operation points. In addition, the control law has engineering practicality since all the variables in the expression of he control strategy can be measured locally.

Data Logging and Control of a Remote Inverter Using LoRa and Power Line Communication

For decades, the power system was highly centralized. With the growing integration of distributed generations into the system, there is a necessity for bi-directional communication methods to monitor and control the remote assets. The primary objective of this paper is to develop a communication link for monitoring and controlling a grid-connected inverter in a remote location. Furthermore, the paper presents developments that have been incorporated to improve the communication link. The literature survey indicates that LoRa is superior compared to other technologies, but has some security and reliability issues. This paper also presents an encryption algorithm to improve the security of the LoRa link. Local data storage added to the system before transmitting data increases the system reliability. A display at the transmission end is added to improve the user-friendliness of the communication link. A Powerline Communication link is parallelly added to the LoRa link to improve the reliability. Finally, tests are conducted with an actual inverter and the results are presented. The tests show that the developed communication link has improved security and reliability, while its open nature makes it highly scalable and adaptable for employment in other smart grid applications.

Frequency Control of Power System with Renewable Power Sources by HVDC Interconnection Line and Battery Considering Energy Balancing

Recently, introduction of renewable energy sources like wind power generation and photovoltaic power generation has been increasing from the viewpoint of environmental problems. However, renewable energy power supplies have unstable output due to the influence of weather conditions such as wind speed variations, which may cause fluctuations of voltage and frequency in the power system. This paper proposes fuzzy PD based virtual inertia control system to decrease frequency fluctuations in power system caused by fluctuating output of renewable energy sources. The proposed new method is based on the coordinated control of HVDC interconnection line and battery, and energy balancing control is also incorporated in it. Finally, it is concluded that the proposed system is very effective for suppressing the frequency fluctuations of the power system due to the large-scale wind power generation and solar power generation and also for keeping the energy balancing in the HVDC transmission line.

Adaptive Robust Control of Multi-Machine Power Systems with Control Time Delay

The adaptive H_∞ control problem of multi-machine power system in the case of disturbances and uncertain parameters is discussed,based on a Hamiltonian model.Considered the effect of time delay during control and transmission,a Hamilton model with control time delay is established.Lyapunov-Krasovskii function is selected,and a controller which makes the system asymptotically stable is got.The controller not only achieves the stability control for nonlinear systems with time delay,but also has the ability to suppress the external disturbances and adaptive ability to system parameter perturbation.The simulation results show the effect of the controller.