Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Two-Dimensional Images of Current and Active Power Signals for Elevator Condition Recognition

In this paper, an improved two-dimensional convolution neural network(2DCNN) is proposed to monitor and analyze elevator health, based on the distribution characteristics of elevator time series data in two-dimensional images. The current and effective power signals from an elevator traction machine are collected to generate gray-scale binary images. The improved two-dimensional convolution neural network is used to extract deep features from the images for classification, so as to recognize the elevator working conditions. Furthermore, the oscillation criterion is proposed to describe and analyze the active power oscillations. The current and active power are used to synchronously describe the working condition of the elevator, which can explain the co-occurrence state and potential relationship of elevator data. Based on the improved integration of local features of the time series, the recognition accuracy of the proposed 2DCNN is 97.78%, which is better than that of a one-dimensional convolution neural network. This research can improve the real-time monitoring and visual analysis performance of the elevator maintenance personnel, as well as improve their work efficiency.

DC active power filter based on model predictive control for DC bus overvoltage suppression of accelerator grid power supply

The China Fusion Engineering Test Reactor plans to build a 200 k V/25 A acceleration grid power supply(AGPS)for the negative-ion-based neutral beam injector prototype system.The AGPS uses a rectifier-inverter-isolated step-up structure.There is a DC bus between the rectifier and the inverter.In order to limit DC bus voltage ripple and transient fluctuations,a large number of capacitors are used,which degrades the reliability of the power supply and occupies a large amount of space.This work finds that due to the difference in the turn-off time of the rectifier and the inverter,the capacitance mainly depends on the rectifier current when the inverter is turned off.On this basis,an active power filter(APF)scheme is proposed to absorb the current.To enhance the dynamic response ability of the APF,model predictive control is adopted.In this paper,the circuit structure of the APF is introduced,the prediction model is deduced,the corresponding control strategy and signal detection method are proposed,and the simulation and experimental results show that APF can track the transient current of the DC bus and reduce the voltage fluctuation significantly.

Coordinated Control Strategy for Harmonic Compensation of Multiple Active Power Filters

In order to minimize the harmonic distortion rate of the current at the common coupling point,this paper proposes a coordinated allocation strategy of harmonic compensation capacity considering the performance of active power filters(APF).On the premise of proportional distribution of harmonic compensation capacity,the harmonic compensation rate of each APF is considered,and the harmonic current value of each APF to be compensated is obtained.At the same time,the communication topology is introduced.Each APF takes into account the compensation ability of other APFs.Finally,three APFs with different capacity and performance are configured at the harmonic source to suppress the same harmonic source,and the harmonic distortion rate is reduced to 1.73%.The simulation results show that the strategy can effectively improve the compensation capability of the multiple APF cascaded system to the power grid without increasing the installed capacity.

Performance Evaluation of a Permanent Magnet Electric-Drive- Reconfigured Onboard Charger with Active Power Factor Correction

This paper presents a comprehensive charging operation for an electric-drive-reconfigured onboard charger(EDROC)with active power factor correction(APFC).The charging topology exclusively utilizes the three-phase permanent magnet synchronous motor(PMSM)propulsion system as a three-channel boost-type converter in which only a contactor and a small diode bridge are added.First,the operation scenario of the EDROC is introduced.Second,the relationship between electromagnetic torque and rotor position is investigated.Third,the current ripple cancellation of the EDROC is discussed in detail.Moreover,to implement the single-phase APFC along with charging voltage/current regulation of propulsion battery,control strategies including current balancing and synchronous/interleaving PWM strategies are incorporated.Finally,200W proof-of-concept prototype-based tests are conducted under different operation scenarios.

Lag compensation errors in active power filters based on DSP controllers

The growing problems of harmonic pollution on coal mine power lines caused by high-power DC drive systems has increased the use of active power filters.We analyzed compensation errors caused by the time lag in the detecting circuits of an active power filter based on DSP control.We derived a mathematical model for the compensation error starting from the error estimation when a single distortion frequency is present.This model was then extended to the case where multiple frequencies are present in the distortion.A formula for a general theory of compensation error with fixed load and fixed lag time is presented.The theoretical analysis and experimental results show that the delay time of an active power filter mainly arises from the sampling time.Lower sampling frequencies introduce larger compensation errors in the active power filter reference current.

A Novel Starting Impulse Suppression Method for Active Power Filter Based on Slowly Rising Curve

With the widespread application of power electronic equipment in the power grid,the harmonic problem of the power grid becomes more pronounced,reducing the efficiency of power production,transmission,and utilization,and interfering with the normal operation of the power grid.Based on the requirements of harmonic suppression and power system protection,a shunt active power filter(SAPF)is proposed as an effective harmonic suppression method.However,there are problems with impulse current and impulse voltage in the starting process of SAPF.Impulse current and impulse voltage cause the power grid and switchgear to bear greater current stress and voltage stress,which seriously affect the security and reliability of the power grid and may damage the switchgear.To effectively solve the problem of impulse current and impulse voltage,the starting process of SAPF is divided into the uncontrolled rectification stage and the transition stage.The mathematical model of the DC side of APF is established.The causes of impulse current and impulse voltage in the uncontrolled rectifier and transition phases are analyzed.By introducing voltage square,a new starting impulse suppression strategy of active power filter based on the slow rising curve is proposed,fundamentally solving the problems of impulse current and impulse voltage.Simulation results verify the effectiveness and feasibility of this method.

Research on Direct Power Control of DC Side for the Active Power Filter

This paper addresses the principle and control topology of active pow er filter( APF). The control strategy of direct pow er control based on DC side is introduced,and the control output is implemented by using the principle of hysteresis control.The active pow er filter can effectively control the pow er system harmonics,improve the quality of electricity and purify pow er grid. The experimental results are provided to illustrate that the direct pow er control based on DC side has the advantages of simple structure,stable DC output voltage and fast dynamic response.

An Efficient Particle Swarm Optimization Technique for 4-Leg Shunt Active Power Filter

This paper describes the mitigation of harmonics in source and neutral current in three phase four wire system based on 4-leg shunt active power filter under balanced and unbalanced load conditions. Particle Swarm Optimization (PSO) and conventional Proportional Integral (PI) controller are used as control techniques to analyze the control performance of 4-leg shunt active power filter. The synchronous reference frame (SRF) method is used to extract reference current in 4-leg shunt active filter. The Hysteresis Current Controller (HCC) is used to generate gate pulses for Voltage Source Inverter (VSI) based 4-leg shunt active power filter. The proposed PSO technique gives less percentage of Total Harmonic Distortion (THD) value in source and neutral current and settling time of the DC capacitor voltage compared to conventional PI controller technique. The model of the proposed system performance was validated using MATLAB/Simulink environment.

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.

UPFC setting to avoid active power flow loop considering wind power uncertainty

The active power loop flow(APLF)may be caused by impropriate network configuration,impropriate parameter settings,and/or stochastic bus powers.The power flow controllers,e.g.,the unified power flow controller(UPFC),may be the reason and the solution to the loop flows.In this paper,the critical existence condition of the APLF is newly integrated into the simultaneous power flow model for the system and UPFC.Compared with the existing method of alternatively solving the simultaneous power flow and sensitivity-based approaching to the critical existing condition,the integrated power flow needs less iterations and calculation time.Besides,with wind power fluctuation,the interval power flow(IPF)is introduced into the integrated power flow,and solved with the affine Krawcyzk iteration to make sure that the range of active power setting of the UPFC not yielding the APLF.Compared with Monte Carlo simulation,the IPF has the similar accuracy but less time.

Voltage sag compensation strategy for unified power quality conditioner with simultaneous reactive power injection

Unified power quality conditioner(UPQC)holds the capability of solving power quality problems,especially shows good performance in the voltage sag compensation. In this paper, a compensation strategy based on simultaneous reactive power injection for UPQC(namely UPQC-SRI) is proposed to address the issue of voltage sag. The proposed UPQC-SRI determines the injection angle of compensation voltage with consideration of optimal configuration of UPQC current-carrying.Moreover, the compensation strategy also considers the current-carrying limit of UPQC, and then the zero active power injection region of UPQC-SRI(also called UPQCSRI region) is obtained. Under the conditions which exceed the UPQC-SRI region, the limit value of shunt current is determined by this proposed strategy. Finally, the proposed strategy and the corresponding algorithm are verified under the PSCAD/EMTDC platform. The result indicates the proposed UPQC-SRI strategy in this paper can provide more persistent voltage sag compensation than the previous strategies for the sensitive load.

Complete active-reactive power resource scheduling of smart distribution system with high penetration of distributed energy resources

In traditional power systems,besides the conventional power plants that provide the necessary reactive power in transmission system,the shunt capacitors along with the tap changers of transformers are also employed in distribution networks.In future years,because of the high number of distributed resources integrated into the distribution networks,it will be essential to schedule complete active-reactive power at distribution level.In this research work,an economic framework based on the active-reactive power bids has been developed for complete active-reactive power dispatch scheduling of smart distribution networks.The economical complete active-reactive power scheduling approach suggested in this study motivates distributed energy resources(DERs)to cooperate in both active power markets and the Volt/Var control scheme.To this end,using DER’s reactive power capability,a generic framework of reactive power offers for DERs is extracted.A 22-bus distribution test system is implemented to verify the impressiveness of the suggested active-reactive power scheduling approach.

High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

Finite control set-model predictive control (FCS-MPC) is employed in this paper to control the operation of a three-phase grid-connected string inverter based on a direct PQ control scheme. The main objective is to achieve high-performance decoupled control of the active and reactive powers injected to the grid from distributed energy resources (DER).The FCS-MPC scheme instantaneously searches for and applies the optimum inverter switching state that can achieve certain goals, such as minimum deviation between reference and actual power;so that both power components (P and Q) are well controlled to their reference values.In addition, an effective method to attenuate undesired cross coupling between the P and Q control loops, which occurs only during transient operation, is investigated. The proposed method is based on the variation of the weight factors of the terms of the FCS-MPC cost function, so a higher weight factor is assigned to the cost function term that is exposed to greater disturbance. Empirical formulae of optimum weight factors as functions of the reference active and reactive power signals are proposed and mathematically derived. The investigated FCS-MPC control scheme is incorporated with the LVRT function to support the grid voltage in fulfilling and accomplishing the up-to-date grid codes. The LVRT algorithm is based on a modification of the references of active and reactive powers as functions of the instantaneous grid voltage such that suitable values of P and Q are injected to the grid during voltage sag.The performance of the elaborated FCS-MPC PQ scheme is studied under various operating scenarios, including steady-state and transient conditions. Results demonstrate the validity and effectiveness of the proposed scheme with regard to the achievement of high-performance operation and quick response of grid-tied inverters during normal and fault modes.

Hybrid intelligence approach for multi-load level reactive power planning using VAR compensator in power transmission network

This paper formulates and solves a techno-economic planning problem of reactive power (VAR) in power transmission systems under loadings. The objective of the proposed research work is to minimize the combination of installation cost of reactive power sources, power losses and operational cost while satisfying technical constraints. Initially, the positions for the placement of reactive power sources are determined technically. Different cost components such as VAR generation cost, line charging cost etc. are then added in the total operating cost in a most economical way. Finally, the optimal parameter setting subjected to reactive power planning (RPP) is obtained by taking advantages of hybrid soft computing techniques. For the justification of the efficiency and efficacy of the proposed approach the entire work is simulated on two inter-regional transmission networks. To validate the robustness and ease of the soft computing techniques in RPP the responses of benchmark functions and statistical proof are provided simultaneously.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part I:A Simulation-driven Solution for Robustness

Employing the novel Deep Reinforcement Learning approach,this paper addresses the active power corrective control in modern power systems.Seeking to minimize the joint effect engendered by operation cost and blackout penalty,this correction strategy focuses on evaluating the robustness and adaptability aspects of the control agent.In Part I of this paper,where robustness is the primary focus,the agent is developed to handle unexpected incidents and guide the stable operation of power grids A Simulation-driven Graph Attention Reinforcement Learning method is proposed to perform robust active power corrective control.The aim of the graph attention networks is to determine the representation of power system states considering the topological features.Monte Carlo tree search is adopted to select the best suitable action set out of the large action space,including generator redispatch and topology control actions.Finally,driven by simulation,a guided training mechanism along with a long-short-term action deployment strategy are designed to help the agent better evaluate the action set while training and to operate more stably when deployed.The efficacy of the proposed method has been demonstrated in the“2020 I earning to Run a Power Network.Neurips Track 1”global competition and the associated cases.Part II of this paper deals with the adaptability case,where the agent is equipped to better adapt to a grid that has an increasing share of renewable energies through the years.

Active Power Reserve Photovoltaic Virtual Synchronization Control Technology

The photovoltaic virtual synchronous generator(PV-VSG)solves the problem of lack of inertia in the PV power-generation system.The existing PV plants without energy storage are required to participate in the power grid’s frequency modulation(FM),but existing PV-VSGs with energy storage have high requirements for coordinated control.Therefore,the active power reserve PV-VSG(APR-PV-VSG)is studied.Based on the different methods to obtain the maximum power point(MPP),the peer-to-peer and master-slave APR-PV-VSG strategies are proposed.The PV inverters are deviated from the MPP to reserve active power,which is used as the virtual inertia and primary FM power.These methods equip the PV power station with FM capability.The effectiveness of the proposed control strategies is verified by simulation results.

Active Power Correction Strategies Based on Deep Reinforcement Learning Part II:A Distributed Solution for Adaptability

This article is the second part of Active Power Correction Strategies Based on Deep Reinforcement Learning.In Part II,we consider the renewable energy scenarios plugged into the large-scale power grid and provide an adaptive algorithmic implementation to maintain power grid stability.Based on the robustness method in Part I,a distributed deep reinforcement learning method is proposed to overcome the infuence of the increasing renewable energy penetration.A multi-agent system is implemented in multiple control areas of the power system,which conducts a fully cooperative stochastic game.Based on the Monte Carlo tree search mentioned in Part I,we select practical actions in each sub-control area to search the Nash equilibrium of the game.Based on the QMIX method,a structure of offine centralized training and online distributed execution is proposed to employ better practical actions in the active power correction control.Our proposed method is evaluated in the modified global competition scenario cases of“2020 Learning to Run a Power Network.Neurips Track 2”.

Impact of the penetration of distributed generation on optimal reactive power dispatch

Optimal reactive power dispatch(ORPD)is a complex and non-linear problem,and is one of the sub-problems of optimal power flow(OPF)in a power system.ORPD is formulated as a single-objective problem to minimize the active power loss in a transmission system.In this work,power from distributed generation(DG)is integrated into a conventional power system and the ORPD problem is solved to minimize transmission line power loss.It proves that the application of DG not only contributes to power loss minimization and improvement of system stability but also reduces energy consumption from the conventional sources.A recently proposed meta-heuristic algorithm known as the JAYA algorithm is applied to the standard IEEE 14,30,57 and 118 bus systems to solve the newly developed ORPD problem with the incorporation of DG.The simulation results prove the superiority of the JAYA algorithm over others.The respective optimal values of DG power that should be injected into the four IEEE test systems to obtain the minimum transmission line power losses are also provided.

Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bioinspired optimization algorithms

Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To address this problem,the present work mainly focuses on optimal integration of distributed generators(DG)into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily(24 h)power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approach:To achieve the desired outcomes,an efficient weighted factor multi-objective function is modeled.Particle Swarm Optimization(PSO)and Butterfly Optimization(BO)algorithms are selected and implemented to minimize the objectives of the system.A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system.The simulations are carried out using MATLAB software.Findings:The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects.Among PSO and BO,BO is comparatively successful in achieving the desired objectives.Originality/value:The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential,industrial and commercial loads.Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.