Small-signal modelling and stability analysis of grid-following and grid-forming inverters dominated power system

In this paper,the explicit state-space model for a multi-inverter system including grid-following inverter-based generators(IBGs)and grid-forming IBGs is developed by the two-level component connection method(CCM),which modularized inverter control blocks at the primary level and IBGs at the secondary level.Based on the comprehensive state-space model representing full order of system dynamics,eigenvalues of the overall system are thoroughly analyzed,identifying potential adverse impacts of not only grid-following inverters,but also grid forming inverters on the system small-signal stability,with the underlying principle of oscillations also understood.Numerical and simulation results validate effectiveness of the proposed methodology on IEEE benchmarking 39-bus system.

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.

Transformer-based correction scheme for short-term bus load prediction in holidays

To tackle the problem of inaccurate short-term bus load prediction,especially during holidays,a Transformer-based scheme with tailored architectural enhancements is proposed.First,the input data are clustered to reduce complexity and capture inherent characteristics more effectively.Gated residual connections are then employed to selectively propagate salient features across layers,while an attention mechanism focuses on identifying prominent patterns in multivariate time-series data.Ultimately,a pre-trained structure is incorporated to reduce computational complexity.Experimental results based on extensive data show that the proposed scheme achieves improved prediction accuracy over comparative algorithms by at least 32.00%consistently across all buses evaluated,and the fitting effect of holiday load curves is outstanding.Meanwhile,the pre-trained structure drastically reduces the training time of the proposed algorithm by more than 65.75%.The proposed scheme can efficiently predict bus load results while enhancing robustness for holiday predictions,making it better adapted to real-world prediction scenarios.

A probabilistic power flow calculation method considering the uncertainty of the static frequency characteristic

In a power system, power generation and load have frequency response characteristics, which randomly fluctuate with changes in operating status. This study investigates a probabilistic power flow method that considers the unit and load uncertainty of the static frequency characteristic. Firstly, a calculation model is established on the basis of the characteristics of the frequency modulation performance of the unit and load. Then a calculation method is developed using the concept of dynamic power flow in order to determine the probability distribution of the active power flow of each line under the occurrence of a fault in the system. In the method, Monte Carlo sampling with the semi-invariant method is applied for analysis and calculation. The IEEE-30-buses system is taken as an example to analyze the impact of different responses of units on the power flow distribution of various branches. The method discussed herein is compared with the Monte Carlo simulation method to verify its effectiveness.

A Quick Classification Method of the Power Quality Disturbances

This paper introduces a quick classification method of the power quality disturbances. Based on analyzing the characteristics of different electrical disturbance signals in time domain, four distinctive features are extracted from electrical signals for classifying different power quality disturbances and then an automatic classifier is proposed. Using the proposed classification method,a PQ monitor of the classifying power quality disturbances is developed based on the TMS320F2812DSP micro-processor. Semi-physical simulation, lab experiment and field measurement results have verified that this proposed method can classify single or complex disturbance signals effectively.

Deep-learning-based methods for super-resolution fluorescence microscopy

The algorithm used for reconstruction or resolution enhancement is one of the factors affectingthe quality of super-resolution images obtained by fluorescence microscopy.Deep-learning-basedalgorithms have achieved stateof-the-art performance in super-resolution fluorescence micros-copy and are becoming increasingly attractive.We firstly introduce commonly-used deep learningmodels,and then review the latest applications in terms of the net work architectures,the trainingdata and the loss functions.Additionally,we discuss the challenges and limits when using deeplearning to analyze the fluorescence microscopic data,and suggest ways to improve the reliability and robustness of deep learning applications.

Construction Method of Equipment Defect Knowledge Graph in IoT

Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect detection.At the same time,different types of intelligent sensors can mon-itor environmental information,such as temperature,humidity,dust,etc.Therefore,we apply the Internet of Things(IoT)technology to monitor the related environment and pervasive interconnections to diverse physical objects.However,the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems.Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph,which can be used to organize,share,and analyze equipment defect-related knowledge.At present,there are a lot of relevant data in the field of intelligent equipment defects.These equipment defect data often focus on a single aspect of the defect field.It is difficult to integrate the database with various types of equipment defect information.This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology.Based on ontology,this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities.This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field.The final experiment proves that this model is superior to other models in precision,recall,and F1 value.This research can break the barrier of multi-source heterogeneous knowledge,build an efficient storage engine for multimodal data,and empower the safety of Industrial applications,data,and platforms in multi-clouds for Internet of Things.

Concept and Research Framework for Coordinated Situation Awareness and Active Defense of Cyber-physical Power Systems Against Cyber-attacks

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system(CPPS),the safe and reliable operation of CPPSs is being increasingly impacted by cyber security.This situation poses a challenge to traditional security defense systems,which considers the threat from only one side,i.e.,cyber or physical.To cope with cyberattacks,this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of CPPSs.An example of a regional frequency control system is used to show the validness and potential of this concept.Then,the research framework is presented for studying and implementing this concept.Finally,key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

Architecture,Key Technologies and Applications of Load Dispatching in China Power Grid

With the development of renewable energy and the changes in the characteristics of power grid,it is becoming increasingly difficult to balance power supply and demand in space and time.In addition,the requirement for improved dispatching capability of power grid is increasing.Therefore,the potential of flexible load dispatching should be realized,which can promote the large-scale consumption of renewable energy and the construction of new power grid.Based on the analysis of existing load dispatching studies and the differences in the characteristics of domestic and foreign load dispatchings,a technical architecture and several key technologies are proposed for load resources to participate in power grid dispatching under the new situation,i.e.,the autonomous collaborative control system of load dispatching.This system implements the multi-layer coordinated control of main,distribution and micro grids(load aggregators).Adjustable load resources are aggregated through an aggregator operation platform and connected with a dispatcher load regulator platform to realize real-time data interaction with dispatching agencies as well as the monitoring,con-trol,and marketing of aggregators.It supports the load resources to participate in network-wide dispatching optimization via continuous power adjustment.Several key technologies such as the control mode,load modeling,dispatching strategy,and safety protection are also elaborated.Through the closed-loop control of orderly charging piles and energy storage clusters in the North China Power Grid,the feasibility of the proposed architecture and key technologies is verified.This route has successively supported multiple adjustable load aggregators to partici-pate in the ancillary services market of North China Power Grid for peak-shaving.Finally,the technical challenges of load resources participating in power grid dispatching under the dual carbon goals are discussed and prospected.

Comparison Between Thermally Stimulated Discharge Method and Photo-stimulated Discharge Method for Measuring Space Charge Traps Energy

The space charge behavior of a dielectric under HVDC is influenced by the charge trap energy distribution in it. Hence, we in- vestigated the charge trap distributions in several kinds of typical polymer materials using thermally stimulated discharge (TSD) and photo-stimulated discharge (PSD) methods, respectively. The experimental results show that,there is a significant difference between the trap energy distributions obtained by the two methods, but the difference decreases with the increase of the melting point of polymers. This is attributed to the change of the trap center environment during TSD caused by the increasing movements of both main chains and branched chains in polymers. PSD method is more accurate for investigating charge trap distribution in dielectrics, especially for polymers with low melting points.

Guest Editorial:Special Section on Control Centers for the Evolving Power Grid:Architecture and Applications

THE world-wide drive to decrease greenhouse gases in the atmosphere is rapidly changing the mix of fuel resources that are used to generate electricity.The percentage of fossil fuels,especially coal,is declining while the percentage of renewable resources,like wind and solar,is increasing.The generation mix has always depended on the avail-ability of different fuels in different regions,like the availability of hydro or the acceptability of nuclear power,and the recent changes also depend on this geography,like the radiance of the sun or the intensity of the wind.Although these changes will lead to different architectures in the evolving power grid,some trends are already being felt.

Multi-channel DMA Based Design of Voltage Monitoring Using Synchronous Frequency Trace-Sampling Technique

In order to eliminate the influence of frequency change on real-time voltage acquisition,a low-cost solution of voltage monitoring was proposed using the multi-channel DMA synchronous frequency trace-sampling technique.In-chip resources of the designed voltage monitor were fully utilized in hardware design to reduce external devices.The MQX RTOS was used to perform the functional tasks flexibly and efficiently;especially the Ethernet communication applications and USB device connection were realized using its TCP/IP protocol stack and USB driver.In addition,to ensure the safety of electrical records,data statistics and alarm information management were also implemented through the management of the storage in FLASH.The test results show that the voltage monitor designed in this paper has the advantages of accurate measurement,strong resistance to frequency interference and low cost,and can be widely applied in the field of voltage monitoring in distribution networks.

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements.These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints,such as the valve point effect,power balance and ramprate limits.The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times.In this paper,multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model.Self-learning teaching-learning based optimization(TLBO)is employed to solve the non-convex non-linear dispatch problems.Numerical results onwell-known benchmark functions,as well as test systems with different scales of generation units show the significance of the new scheduling method.

Cyber-attack-tolerant Frequency Control of Power Systems

Cyber attacks are emerging threats in the Internet of Things applications,and power systems are typical cyber attack targets.As one of the most essential operation functions,frequency control is threatened by cyber intrusions,and the existing centralized control mode cannot effectively address cyber risks.In this study,a new distributed cyber-attack-tolerant frequency control scheme is designed.The distributed control mode also serves as a convenient tool for attack identification.The designed cyber-attack-tolerant frequency controller adopts the idea of passive fault attenuation,thus simplifying the design procedure.With the aid of graph theory and consensus techniques,distributed integral based and model predictive control(MPC)based controllers are designed.Compared with the integral type,the MPC-based controller can simultaneously improve the dynamic responses and the tolerance ability under attack.The proposed controller is validated via an IEEE benchmark system,and the effectiveness of its application in actual power systems is verified.

Intelligent Assessment of Active and ReactivePower Flow with Satisfying Accuracy forN-k_(1)-k_(2) Cascading Outages

Addressed to the N-k_(1)-k_(2) cascading outages,it is computationally burdensome for the reliable calculation of active and reactive power flows.This paper builds a comprehensive framework with three algorithms,including the distribution factor(DF),the Newton-Raphson(NR),and the first iteration of NR algorithm(termed as 1J).Classifiers are designed to determine whether the NR algorithm should be employed for accuracy.Classifier features are extracted upon the analytical error of 1J.As reactive power is partially considered in the 1J but neglected in the DF algorithm,the deviation between the solutions is taken as one crucial feature.The support vector machine(SVM)is then utilized for classifier training.As the deep integration of the causal inference and the statistical paradigm,this framework calculates active and reactive power flows rapidly,reliably,and robustly.The effectiveness and robustness are fully validated in three typical IEEE systems.

Fully Distributed Economic Dispatch for Cyber-physical Power System with Time Delays and Channel Noises

Economic dispatch problem(EDP)is a fundamental optimization problem in power system operation,which aims at minimizing the total generation cost.In fact,the power grid is becoming a cyber-physical power system(CPPS).Therefore,the quality of communication is a key point.In this paper,considering two important factors,i.e.,time delays and channel noises,a fully distributed consensus based algorithm is proposed for solving EDP.The critical maximum allowable upper bounds of heterogeneous communication delays and self-delays are obtained.It should be pointed out that the proposed algorithm can be robust against the time-varying delays and channel noises considering generator constraints.In addition,even with time-varying delays and channel noises,the power balance of supply and demand is not broken during the optimization.Several simulation studies are presented to validate the correctness and superiority of the developed results.

Weakly supervised temporal action localization with proxy metric modeling

Temporal localization is crucial for action video recognition.Since the manual annotations are expensive and time-consuming in videos,temporal localization with weak video-level labels is challenging but indispensable.In this paper,we propose a weakly-supervised temporal action localization approach in untrimmed videos.To settle this issue,we train the model based on the proxies of each action class.The proxies are used to measure the distances between action segments and different original action features.We use a proxy-based metric to cluster the same actions together and separate actions from backgrounds.Compared with state-of-the-art methods,our method achieved competitive results on the THUMOS14 and ActivityNet1.2 datasets.

Simulation analysis on policy interaction effects between emission trading and renewable energy subsidy

Greenhouse gas emission regulation and renewable energy promotion policies have been implemented in many countries.Yet these two kinds of regulation policies have complex interactions between each other,and can either enhance or reduce the overall emission reduction efficiency.If not well tuned,these regulation policies may deviate from their original intention and lead to unnecessary social cost.Hence,the policy effectiveness,cost effectiveness,and dynamic efficiency of different policy mixtures between emission trading and renewable energy subsidy are studied based on a novel dynamic simulation platform of power economy and power system.Simulation results show that these two kinds of regulation policy can coexist,but a good coordination between the emission trading and the renewable energy subsidy can achieve better emission reduction outcomes.

A security scheme for intelligent substation communications considering real-time performance

Tampering,forgery and theft of the measurement and control messages in a smart grid could cause one breakdown in the power system.However,no security measures are employed for communications in intelligent substations.Communication services in an intelligent substation have high demands for real-time performance,which must be considered when deploying security measures.This paper studies the security requirements of communication services in intelligent substations,analyzes the security capabilities and shortages of IEC 62351,and proposes a novel security scheme for intelligent substation communications.This security scheme covers internal and telecontrol communications,in which the real-time performance of each security measure is considered.In this scheme,certificateless public key cryptography(CLPKC)is used to avoid the latency of certificate exchange in certificate-based cryptosystem and the problem of key escrow in identity-based cryptosystem;the security measures of generic object-oriented substation event,sampled measure value and manufacturing message specification in IEC 62351 are improved to meet the real-time requirements of the messages as well as to provide new security features to resist repudiation and replay attacks;and the security at transport layer is modified to fit CLPKC,which implements mutual authentication by exchanging signatures.Furthermore,a deployment of CLPKC in an intelligent substation is presented.We also evaluate the security properties of the scheme and analyze the end-to-end delays of secured services by combining theoretical calculation and simulation in this paper.The results indicate that the proposed scheme meets the requirements of security and real-time performance of communications in intelligent substations.

A sensorless efficiency test system for a high-speed permanent magnet synchronous motor

We present a sensorless efficiency test system with energy recovery for a high-speed permanent magnet synchronous motor(PMSM). In the system, two identical high-speed PMSMs are used as the motor under test(MUT)and the load machine(LM),respectively.A new sensorless vector control(VC) method based on a hypothetical reference frame is presented to control both the MUT and the LM.Also,a regenerating unit is used to implement energy circulation to save energy.Experiments were carried out on a prototype, with a digital controller based on the TMS320 F28335, to verify the adequacy of the sensorless VC method.As a result,the efficiency test system achieves the load test at the speed of 21000 r/min without any reduction equipment. During the test, the energy regenerated by the LM could be fed back to the MUT by the regenerating unit, and 81.31% electrical power was saved.In addition, with the proposed sensorless VC method,both the MUT and the LM can work at i_d = 0 without a position sensor.