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A method for estimation of remaining useful life or state of health of an electronic device, such as a battery, the method comprising the steps of: estimating state of charge (SoC) of the electronic device based on measured voltage, and/or current, and/or cell temperature using a machine learning state of charge estimation model;5estimating an accumulated damage in the electronic device based on the output of the machine learning state of charge estimation model and a machine learning incremental damage model, wherein the machine learning incremental damage model is trained with laboratory data and real life and/or field operation data from the electronic device itself and/or from similar electronic devices; and based on the accumulated damage, 10estimating the remaining useful life or the state of health of the electronic device.

The invention regards a method for identifying outliers in a population of electronic devices, the method comprising the steps of: providing a plurality of first machine learning models, each first machine learning model associated with one electronic device in a population of electronic devices, wherein each first machine learning model is incrementally trained based on estimated input power loss and/or input ambient temperature and/or output heat sink temperature and/or voltage of each electronic device; providing a second machine learning model, the second machine learning model being based on a federated average of model parameters of a plurality of, such as a majority of, or all of, the first machine learning models; and comparing the second machine learning model with each of the first machine learning models to identify outliers among the population and wherein the electronic devices are power converters or power switches or batteries or battery systems used for power applications.

Aiming at the instability problem of traditional power synchronization control (PSC) in a stiff grid, an advanced PSC based on modular multilevel converter (MMC) is proposed in this paper, which can effectively improve converter stability in high short circuit ratio (SCR) scenarios. The key point of the proposed PSC is to eliminate the impact of the arm inductor voltage and capacitor voltage ripple on the converter output voltage, which means a generalized decoupling process in the PSC controller. Based on the decoupling correction, MMC can be treated as a controlled voltage source regardless of the internal impedance circuit and the power oscillation between MMC and the stiff grid can be suppressed effectively. The control strategy is verified and compared with traditional PSC in time simulations.

Multibus dc microgrids, which combine renewable energy sources, energy storage systems, and loads, have voltage stability requirement, which solicits increasing research attention in practice. Potentially complex architectures of the multibus dc microgrids make it difficult to evaluate the stability using the conventional stability criteria. In this article, some constraints related to the conventional stability criteria, such as right-half-plane (RHP) poles or zeros in the subsystems are discussed. Further, an impedance-based stability criterion is proposed in the light of generalized bode plots for multibus dc microgrids. The configuration of the multibus dc microgrid is simplified by adopting generalized voltage source, generalized current source, and two-port model. Then, impedances or admittances for each bus port can be derived, which are helpful for assessing the stability of the system. Using the proposed stability criterion, the stability of each bus port in the multibus dc microgrid can be evaluated separately. The proposed stability method considers the number of RHP pole of the open-loop transfer function for the system so the stability of the system consists of subsystems with RHP pole and zero can be analyzed correctly. Moreover, the intermittent bus converter connected with neighboring dc microgrid can be regarded as an extension unit. Then, the proposed method can be easily extended and is acting as a generalized approach for different configurations, i.e., single-bus dc mirogrid or a cluster of dc microgrids. Experiments are done to validate the effectiveness of the proposed criterion.

This paper investigates and quantifies the consequences of ignoring power converter failures in the system-level design of microgrids. To achieve this goal, the process of converter-level reliability modeling, calculating the availability, and finally obtaining the system-level reliability indices are explained. A model-based and mission profile-based system-level design methodology has been used, which considers both reliability and cost. Further, the methodology has been implemented in a case study, once considering and once ignoring the power electronic failures. Accordingly, it has been shown that ignoring the converter failures can lead to an unreliable system-level design and jeopardizes its cost-effectiveness in the long run.

To cope with the weak grid stability issue of grid-tied Voltage Source Converters (VSCs), this letter proposes an Artificial Neural Network (ANN)-based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the systems poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this work. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.

Differential protection, as the key protection element in the power transformers, has always been threatened with sending false trips subjected to external transient disturbances. As a result, differential protection needs an additional block to distinguish between internal faults and external transient disturbances. The protection system should i) be able to perform based on raw data, ii) be able to learn fully temporal features and sudden changes in the transient signals, and iii) impose no assumption on noise. To address these challenges, a fast recurrent neural network, namely fast gated recurrent neural network (FGRNN). By removing the reset gate in the gated recurrent unit (GRU), the proposed network is capable of learning abrupt changes in addition to significantly reducing the computational time. Furthermore, a loss function based on an information theory concept is formulated in this paper to enhance the learning ability as well as robustness against non-Gaussian/Gaussian noises. A generalized form of mutual information is also adopted to form a noise model-free loss function, then incorporated with the designed deep network. Simulated and experimental examinations engaging various external factors, in addition to a comparison between the proposed fast GRNN, GRU, and seven firmly-established methods indicates the faster and more reliable performance of the proposed algorithm.

Distributed control of modular multilevel converter (MMC) submodules (SMs) offers several potential benefits such as flexibility, scalability, and modularity. In this approach, low-level control tasks, such as capacitor voltage balancing, can be distributed amongst controllers placed in the SMs. This decreases the computational burden for the central control system that performs high-level control tasks; also, a single point of failure is avoided. Distributed control architecture requires a cyber-physical network (CFN) through which local controllers share all the information necessary to perform their respective control loops. To date, none of the reported works in this field have paid attention to potential imperfections in the CFN. Indeed, previous works are based on the assumption that the network always provides correct information to the local controllers. However, erroneous measurements in the CFN may degrade the distributed control scheme operation, leading to suboptimal or even unstable operation. These events can occur in the presence of cyberattacks, for example, which can be created through illegitimate data intrusion into the distributed control architectures. This article is the first to investigate the impacts of cyberattacks on distributed control schemes used in MMCs. The effects of a specific cyberattack, named false data injection attack (FDIA), on a consensus-based distributed control strategy are studied in this article. Additionally, a method for detecting FDIAs is proposed, along with a countermeasure strategy, to ensure the safe operation of the MMC, while the attack is cleared. The proposals reported in this article are validated using simulation and experimental results.

In this paper, a data-driven cyber-attack detection method for islanded DC microgrids is proposed. Data is collected by monitoring the behavior of an intelligent attacker who is able to bypass conventional cyber-attack detection algorithms and disrupt the operation of the system. Reinforcement learning (RL) algorithm emulates the actions of such intelligent attacker, who exploits the vulnerability of index-based cyber-attack detection methods, such as discordant detection algorithm. The data is then used to train a neural network based detector that complements the conventional method with additional capability to detect a larger set of possible attacks. Through experiments, the effectiveness of the proposed method is validated.

The open-circuit fault detection and localization (FDL) technique can improve the reliability of the modular multilevel converter (MMC). However, the conventional software-based FDL methods usually have a heavy computation burden or a limited localization speed. This paper proposes a simplified and fast software-based FDL approach for the grid-connected MMC. Firstly, the errors between the measured state variables (the output current and the circulating current) and their estimated values are calculated. By comparing these errors with their threshold values, the switch fault can not only be detected, but also be localized to the specific arm. Then, the capacitor voltages in this faulty arm are collected, and the submodule (SM) with the highest capacitor voltage is selected. To confirm the switch fault in this SM, a modified Pauta criterion is presented to check the abnormal voltage data. As a result, the computation burden of the proposed software-based FDL approach is significantly reduced, and the faulty SM can be localized in a short period. Simulation and experimental results verify that the proposed approach can effectively detect and locate different open-circuit faults, and it is immune to the step of power references.

DC microgrids can be operated under a hierarchical control strategy, and it needs a communication-based layer. The implementation of digital controllers and the communication infrastructure can make a dc microgrid vulnerable to cyber-attacks. This article introduces an approach based on Artificial Intelligence (AI) to detect and mitigate cyber-attacks in a dc microgrid. The proposed method is based on the artificial neural network (ANN), which can be categorized as an AI-based method. The proposed application implements an ANN to detect and mitigate false data injection attacks (FDIAs). FDIAs try to inject false data into the system to affect the control application of the dc microgrid, and it can shut down the dc microgrid. The proposed method can calculate the value of the false data, and it can detect and remove the attack simultaneously. The proposed method is tested in a MATLAB/Simulink environment. Also, to have more accurate results, the introduced approach is examined under different conditions and cyber/physical disturbances (e.g., communication delay, noise, plug-and-play of additional units, and time-varying FDIAs). Besides, a comparison is considered to evaluate the effectiveness of the proposed strategy. The obtained results can conclusively prove the effectiveness, accuracy, and authenticity of the proposed method to successfully detect the FDIAs and remove the cyber-attack.

Three-phase voltage source inverters (VSIs) with output LC filter are preferred topologies to provide voltage and frequency support for islanded ac microgrids. This article proposes a finite-set quasi-sliding model predictive control (FS-QSMPC) scheme for LC-filtered VSIs to improve the output-voltage quality. By explicitly including a predictive sliding-mode function into the cost function, reduced steady-state ripple and enhanced robustness are achieved compared to typical FS-MPC. Besides, theoretical analysis of stability and robustness for FS-QSMPC is given. Analytical tuning of the weighting factor is also derived to decrease the tuning effort. Comparative simulations and experiments verify the presented approach.

Dual Active Bridge is often considered in many DC/DC converter applications where high efficiency, input/output voltage ratio and power level are required. Recently a predictive control technique, named Moving Discretized Control Set - Model Predictive Control has been introduced, in order to achieve faster dynamics and stable operation throughout the power and terminal voltage ranges, considering global control parameters. However, even if this technique provides flexible multi-objective control, its large signal stability remains in questions. In this paper, the proposed control design is described in details and its large signal stability is analyzed, considering the case of a Dual Active Bridge converter operating in single phase shift. The obtained results show insights into factors that stabilize or destabilize the converter operation. The proposed control and stability analysis is validated through both simulation and experimental testing.

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

Grid-connected power electronic converters are the fundamental building blocks of future power electronic-based power systems. Their fast dynamic performance and robustness are the key requirements for ensuring the reliable operation of such systems. This letter proposes a simple optimal model predictive controller (MPC) to control the active and the reactive current of the three-phase voltage source grid-connected converters. Better performance over other state-of-the-art controllers is evaluated based on quantitative criteria comprising the settling time, and the percentage of the overshoot. Finally, the experimental tests are provided to prove the applicability of the proposed approach.

The increasing power levels handled by electric vehicle dc fast chargers will impose additional challenges to the switching devices in order to cope with the efficiency requirements. A cost-effective alternative to achieve highly efficient power conversion is through the partial-power conversion concept. This paper validates the advantages of a step-down Type II partial-power converter (PPC), based on the phase-shifted fullbridge converter, for EV Fast Chargers. By exploiting the reduced voltage range of an EV battery pack along with the reduced power ratio for a Type II PPC, an extremely efficient charging process can be achieved. The concept is validated with the development of a 7 kW demonstrator, hence realistic efficiency measurements are obtained. Results indicate the effectiveness of charging a battery by merely handling 13.32% of the power provided to it, with a peak efficiency of 99.11%

The parallel connection of DC-DC converters requires the development of an appropriate control strategy that regulates load voltage and shares current amongst participating converters. This paper proposes a resilient and robust cooperative distributed control approach that simultaneously ensures voltage regulation and balanced current sharing in parallel DC-DC converters in the presence of false data injection attacks on control input channels. Based on analytical tools from network control and Lyapunov stability theory, concise stability certificates are derived. The proposed cooperative distributed control strategy guarantees resilience against unknown bounded attacks on the actuators of DC-DC converters and the robustness to uncertainties in load parameters and the physical parameters of converters. Furthermore, the control design for each converter does not require any knowledge about the number of participating converters. The detailed simulation and experimental results verify the satisfactory performance of the proposed method in voltage regulation and balanced current sharing in parallel converters, as well as resilience to bounded false data injection attacks.

Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc microgrids are also vulnerable to cyber-attacks. False-data injection attacks (FDIAs) are a common type of cyber-attacks, which try to inject false data into the system in order to cause the defective behavior. This article proposes a method based on model predictive control (MPC) and artificial neural networks (ANNs) to detect and mitigate the FDIA in dc microgrids that are formed by parallel dc–dc converters. The proposed MPC/ANN-based strategy shows how MPC and ANNs can be coordinated to provide a secure control layer to detect and remove the FDIAs in the dc microgrid. In the proposed strategy, an ANN plays the role of the estimator to implement in the cyber-attack detection and mitigation strategy. The proposed method is examined under different conditions, physical events and cyber disturbances (i.e. load changing and communication delay, and time-varying attack), and the results of the MPC-based scheme is compared with conventional proportional-integral controllers. The obtained results show the effectiveness of the proposed strategy to detect and mitigate the attack in dc microgrids.

A type of peak-value dc voltage control (denoted as PK control) was proposed in the literature for supporting the use of a smaller dc-side capacitance when the single-phase Voltage Source Converter (VSC) is operated as a Static Synchronous Compensator (STATCOM). Although it was demonstrated to operate stably under several conditions, it will be revealed in this paper how the PK control will suffer from a more severe small-signal stability issue under non-ideal grid conditions than the conventional method of controlling the averagevalue of the dc voltage (denoted as Avr control). Especially, it will be shown how the PK control is sensitive to some of the control parameters. To obtain these results, a parameter-oriented stability analysis method is developed in the linear-time periodic (LTP) framework. Then, it is utilized for parametric stability assessments of the Avr and PK control. Finally, both frequency- and time-domain experimental results verified the effectiveness and accuracy of the applied method in the presented analysis.

Virtual synchronous generator (VSG) control technique is widely used for the grid-friendly integration of renewable energy. However, when VSG is attached to a weak grid, it is prone to voltage and frequency instability, which aggravates the voltage quality and frequency quality at the point of common coupling (PCC), and in turn, pollutes the supplying quality for the loads. On the one hand, focusing on voltage stability, this paper proposes the impedance modeling for VSG in the synchronously inertial reference coordinates (SIRC) to measure the voltage stability of grid-tied VSG and identify the interaction between VSG and the weak power grid. Studies show that weak grid strength can lead to a wide-band oscillation of VSG. On the other hand, focusing on frequency instability, the motion equation has been put forward to illustrate the oscillation and dynamic deviation of frequency. The relationship between synchronizing ability of VSG and grid stiffness is discussed. Besides, this paper proposes an inertia-damping-strengthened control method to improve frequency stability. Finally, the theoretical analysis is compared with simulations and experiments.

A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner's commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods.

Finite set model predictive control (FS-MPC) has been identified as one of the most favorable controllers for power electronic applications due to its capability over real-time solutions to multiple objectives and constraints. However, the main challenge in the FS-MPC is the choice of appropriate weighting factors in the cost function to reach the best switching state of the inverter. This study proposes an approach based on brain emotional learning (BEL) to provide online tuning of weighting factors in FS-MPC of a power converter, which prevents the dependency of the converter control system on the various uncertainties coming from operating conditions and loading conditions. The proposed BEL approach is fully model-free, indicating that the weighting factors are adjusted without previous knowledge of the system model and parameters. Simulation and experimental results validate the proposed control scheme’s effectiveness under different load conditions.

Sharp LC resonance peak may enlarge inherent harmonics, dominant harmonics of the supply voltage generated by a three-phase diode rectifier, around the resonance frequency, which will intensify fluctuations of the DC-link voltage and grid currents in motor drives with small DC-link capacitor. In this paper, a novel resonance suppression strategy based on inherent harmonics impedance shaping is proposed specially for three-phase input PMSM drives with small DC-link capacitor. Instead of increasing the damping of the whole system for resonance suppression, the motor side power disturbance can be reduced by designing the impedance for inherent harmonics around LC resonance frequency, based on the new model with the consideration of motor side characteristic. For damping current generation, the angle of the virtual admittance is modified to eliminate the influence of the DC-link voltage sampling delay. Experimental results show the effectiveness of the method on a prototype of electrolytic capacitorless PMSM drive.

The rainflow algorithm is one of the most commonly used tools for studying stress conditions of a wide variety of systems, including power electronics devices and electrochemical batteries. One of the main drawbacks of the algorithm is the trade-off between data compression and the loss of information when classifying the stress cycles into a finite amount of histogram bins. This paper proposes a novel approach for classifying the stress cycles by using fuzzy logic in order to reduce the quantization error of the traditional histogram-based analysis. The method is tested by comparing the accumulated damage estimations of two support-vector regression algorithms when trained with each type of cycle-counting procedure. NASA’s randomized battery usage data set is used as source of stress data. A 50% error reduction was observed when using the fuzzy logic-based approach compared to the traditional one. Thus, the proposed method can effectively improve the accuracy of diagnosis algorithms without penalizing their performance and memory-saving features.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, This paper proposes an observer-based methodology to detect and mitigate false data injection attacks in collaborative DC microgrids. The ability of observers to effectively detect such attacks is complicated by the presence of unknown non-linear constant power loads. This work determines that, in the presence of unknown constant power loads, the considered attack detection and mitigation problem involves non linearities, locally unobservable states, unknown parameters, uncertainty and noise. Taking into account these limitations, a distributed non linear adaptive observer is proposed to overcome these limitations and solve the concerned observation problem. The necessary conditions for the stability of the distributed scheme are found out. Moreover, numerical simulations are performed and then validated in a real experimental prototype, where communication delay, uncertainty and noise are considered., Peer Reviewed, Postprint (author's final draft)

Virtual synchronous generator (VSG) has been a grid-friendly integration control technique for the integration of grid-connected inverters. However, the emulated inertia and damping of VSG control technique can also be used in the field of DC systems. In this paper, a virtual synchronous control is proposed to dampen the wideband oscillation of DC voltage in a DC microgrid. The proposed control strategy contributes to maintaining synchronous operation of DC converter with the network. Besides, the relationships among damping, inertia, wideband oscillation, rate of change of voltage (RoCoV) as well as DC voltage nadir (DCVN) are studied. It is concluded that the RoCoV and DCVN are similarly as the oscillation frequency and fluctuation ranges of poorly-damped oscillation, respectively. A unified concept is proposed by connecting the oscillation-related stability with inertial transient response originated from the imbalanced powers/mismatched currents. Besides of this, the inertia plays the same role as damping because the inertia contributes to maintaining the original state and damping to impeding further change. A new feedback analytical method is proposed to clarify the important role of RoCoV and DCVN on the motion of DC voltage. Finally, the theoretical results are compared with simulations and experiments.

This paper proposes a novel approach to uncover deficiencies of the existing cyber-attack detection schemes and thereby to serve as a foundation for establishing more reliable cybersecure solutions, with particular application in DC microgrids. For this purpose, a multi-agent deep Reinforcement Learning (RL) based algorithm is proposed to automatically discover the vulnerable spots on the conventional index-based cyberattack detection schemes, and automatically generate coordinated stealthy destabilizing False Data Injection (FDI) attacks on cyberprotected islanded DC microgrids. To enable a continuous action space for the trained RL agents and enhance the algorithm’s precision and convergence rate, Deep Deterministic Policy Gradient DDPG) is incorporated. Using this approach, susceptibility of a state-of-the-art detection scheme to several different coordinated FDI attacks on the distributed communication links is identified. The proposed algorithm is also enhanced with a sniffing feature to enable maintaining the stealthy attacks even under the sudden disconnection of any of the compromised links. To address the discovered deficiencies within the index-based detection scheme, a complementary multi-agent RL detection algorithm using Deep Q-Network (DQN) is integrated, which provides a more reliable overall identification performance. Taking into account the communication delays and load changes, the effectiveness of the proposed algorithm is verified by the experimental tests.

In this paper, a cyber-resilient consensus based distributed control scheme is proposed for islanded AC microgrids. Considering the impacts of False Data Injection (FDI) attacks on the conventional consensus algorithms, a hybrid solution is presented based on the combination of multi-objective sliding mode control and communication link quality observer to provide a reliable performance against different types of FDIs. Unlike the commonly developed distributed observer based cyber-resilient algorithms, this approach aims to form a complete localized solution without the requirements for transmission of extra distributed signals in the secondary layer. Using the proposed method, the system reliability will not be impacted by the cyber intrusions on the distributed observer terms, and the system’s dynamic performance is not deteriorated by the counteracting operation resulted from adaptive distributed observers under other system transient conditions such as load dynamics, which can be even worsened under communication delays. Using cyber-resilient offset compensation terms, performance of the proposed sliding mode control scheme is enhanced for multiobjective regulation. Integrating the sliding mode control with communication link observer also ensures effective isolation of the unbounded and extreme cyberattacks. Unlike the former indirect local observers, the proposed scheme operates directly based on monitoring the affected distributed signals rather than only power terms, which enhances its reliability on detection. The effectiveness of the proposed scheme is validated through a realtime model developed in Typhoon HIL-402 real-time simulator as well as the experimental tests.

This paper presents the application of Model Predictive Control (MPC) in high-performance drives. A wide variety of machines have been considered: induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the paper is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.

This paper develops a novel decentralized controller for multi-area secondary load frequency control (LFC) of power systems. The proposed robust-adaptive approach estimates the external disturbance input and uncertainties, which are assumed to be matched with the control input. In this regard, a disturbance-observer state-feedback controller is designed. To offer a systematic approach, controller design conditions are derived in terms of linear matrix inequality (LMI) constraints. Thereby, for any given representation of the LFC area, the controller gains can be straightforwardly obtained by using numerical solvers. Moreover, in order to enhance the steady-state performance, the controller is modified through the heuristic genetic algorithm (GA). The controller design procedure is wholly offline and can be simply implemented. The developed decentralized approach does not need the information of the other areas, which reduces the cost and the number of measuring units. It is also robust against the power fluctuations of the load and renewable energy sources. To show the superiorities of the developed controller, four scenarios are considered. These scenarios comprise the aggregation of the photovoltaic, wind turbines, and electric vehicle (EV) to the power system. OPAL-RT experiments are given to verify the transient and steady-state performance and robustness of the proposed controller.

Virtual synchronous generator (VSG), recognized as a grid-friendly control technique, contributes to providing inertia and damping support from the grid-tied power electronic systems. However, similar to conventional synchronous generator (SG), the VSG is vulnerable to wideband oscillations, and can even become unstable when the system is subjected to a variety of disturbances. To tackle this issue, this paper proposes a magnitude-phase motion equation (MPME) for grid-forming VSG attached to weak grid. Besides, a feedback analytical method is introduced to identify the frequency dynamic stability, judged by the sign of (d/dt)/. Besides, the rate of change of frequency (RoCoF) and frequency nadir (FN) are evaluated by the two indices, i.e., d/dt and . In addition, the relationship between low-inertia-RoCoF-FN-related stability and the weak-damping-oscillation-related stability is illustrated by the feedback analysis. Moreover, in the framework of MPME, a reactive-power feedforward (PPFF) decoupling control strategy is proposed for enhancement of stability and robustness performance of the VSG-system. Finally, the proposed modeling and the reactive-power feedforward decoupling control strategy are demonstrated with the experiments.

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation become inaccurate which leads to wrong supervisory decision making. To detect cyber-attacks in power grids equipped with PMUs, a new intrusion detection system based on clustering approach (PMUIDS) is proposed. After solving the optimal PMU placement in N-1 contingency, several state estimations are obtained by removing the measurements of one PMU in each time. The resulting state vectors are clustered in two steps: Subtractive clustering is employed to obtain the number of clusters which determines the number of integrity attacks, Fuzzy C-means clustering assigns the state vectors to the corresponding clusters which determines the attacked PMUs. Also, two theorems are proved which indicate that the attacker cannot coordinate successful stealth attacks in cases that by removing attacked PMUs from state estimation, the power system still remains fully observable. Furthermore, in the case of possible stealth attacks, the attacker cannot falsify the estimation of any arbitrary state variable. The hardware-in-the-loop results on a power system show that the proposed approach detects integrity attacks, determine the number of attacks, obtain the correct state vector, and localize the attacks in case of multiple simultaneous attacks.