The sudden outage of a transformer due to a fault can cause irreparable damage to the electricity industry. Hence, by conducting momentarily inspections of the transformer's condition, faults can be promptly detected, and the transformer can be disconnected from the power grid to prevent subsequent failures in this equipment. Detecting faults at an early stage can also result in reduced repair costs. One recent promising technique for fault detection is Frequency Response Analysis (FRA), which compares the transformer's response in healthy and faulty conditions for understanding the occurrence of transformer faults. This paper presents a comprehensive and accurate modeling approach for the behavior of the transformer at different frequencies, followed by an exposition of the requirements for implementing this method in order to find the fault type, severity, and location. Additionally, various methods for analyzing the results of frequency response are introduced and discussed. In this regard, attempts have been made to introduce advanced complementary methods to address the weaknesses and limitations of the frequency response method. Finally, the concepts are summarized, and suggestions for further research with applications in this field are presented and compared.

Nowadays, modern power converters installed in renewable power plants can provide flexible electromechanical characteristics that rely on the developed control technologies such as Synchronous Power Controller (SPC). Since high renewable penetrated power grids result in a low-inertia system, this electromechanical characteristic provides support to the dynamic stability of active power and frequency in the power generation area. This goal can be achieved through the proper tuning of virtual electromechanical parameters that are embedded in the control layers of power converters. In this paper, a novel mathematical pattern and strategy have been proposed to adjust dynamic parameters in Renewable Static Synchronous Generators controlled by SPC (RSSGSPC). A detailed dynamic modeling was obtained for a feasible design of virtual damping coefficient and virtual moment of inertia in the electrometrical control layer of RSSG-SPC’s power converters. Mathematical solutions, modal analysis outcomes, time-domain simulation results, and real-time validations of the test in IEEE-14B benchmark confirm that the proposed method is an effective procedure for the dynamic design of RSSG-SPC to provide these dynamic stability supports in grid connection.

This paper presents a new application of advanced SMPI controller for a newly introduced interconnected dynamic system with VSP based HVDC links for frequency control problem. This work presents an outgrowth of analysis about the Swarm – Based Optimization Algorithms (SBOAs) in the tuning process of Multivariable Proportional – Integral (MPI) controller, which is called swarm – based MPI (SMPI). PSO, GOA and GWO algorithms are used for tuning process of the designed SMPI. The VSP based HVDC model is added for mitigation of system frequency dynamics with emulating virtual inertia. The proposed SMPI controller are designed for enhancing the dynamic performance of this system's states during contingencies and they are compared with the conventional designed MPI controller. Deviation characteristics of the step function in MIMO transfer function of the VSP based HVDC model is considered as the common performance index in the proposed algorithms. On the other hand, the role of the proposed SMPI controller is to stabilize such interconnected system while minimizing the associated cost function. Matlab simulations next to the performed Nyquist's stability anslysis demonstrate how the tuned SMPI can remarkably improve the frequency deviations and the damping of the inter-area oscillations excited during a fault. This enhancement is more obvious especially when SMPI controller for a VSP based virtual inertia emulation is tuned using GWO method.

The high penetration of renewable energy resources and power electronic-based components has led to a low-inertia power grid which would bring challenges to system operations. The new model of load frequency control (LFC) must be able to handle the modern scenario where controlled areas are interconnected by parallel AC/HVDC links and storage devices are added to provide virtual inertia. Notably, vulnerabilities within the communication channels for wide-area data exchange in LFC loops may make them exposed to various cyber attacks, while it still remains largely unexplored how the new LFC in the AC/HVDC interconnected system with emulated inertia would be affected under malicious intrusions. Thus, in this article, we are motivated to explore possible effects of the major types of data availability and integrity attacks—Denial of Service (DoS) and false data injection (FDI) attacks—on such a new LFC system. By using a system-theoretic approach, we explore the optimal strategies that attackers can exploit to launch DoS or FDI attacks to corrupt the system stability. Besides, a comparison study is performed to learn the impact of these two types of attacks on LFC models of power systems with or without HVDC link and emulated inertia. The simulation results on the the exemplary two-area system illustrate that both DoS and FDI attacks can cause large frequency deviations or even make the system unstable; moreover, the LFC system with AC/HVDC interconnections and emulated inertia could be more vulnerable to these two types of attacks in many adversarial scenarios.

Participation of wind energy in the generation portfolio of power systems is increasing, making it more challenging for system operators to adequately maintain system security. It therefore becomes increasingly crucial to accurately predict the wind generation. This work investigates how different parameters influence the performance of forecasting algorithms. Firstly, this work analyzes the combined influence of the input data, batch size, number of neurons and hidden layers, and the training data on the forecast accuracy across forecast horizons of 5, 15, 30 and 60 min. It was found that increasing look ahead times require among others more hidden layers and lower batch sizes. Next, the optimizer and loss function leading to the most accurate forecasts were identified. It was concluded that the Adadelta optimizer and Mean Absolute Error loss function consistently result in the best performing forecasting algorithm. Finally, it was investigated if the most accurate optimizer-loss function combination is influenced by the choice of the performance metric. Whereas the Adadelta-Mean Absolute Error pair remains the most accurate combination irrespective of the evaluation metric, a strong relation was observed between the Root Mean Square Error performance metric and Mean Square Error loss function. Analyses were performed on 12 wind farms.

Unlike synchronous generators, wind turbines cannot directly respond to large disturbances, which may cause transient instability, due to their power electronic-based interface and maximum power control strategy. To effectively monitor the influence of wind turbines, this paper proposes an approach that combines decision trees (DTs), and a newly developed variant of the Mean-Variance Mapping Optimization (MVMO) algorithm, to simultaneously tackle the problem of selecting the key variables that properly reflect the transient stability performance of a system dominated by wind power, and designing the DTs for reliable online assessment of transient stability. The notion of key variables refers to the set of variables that are closely related to the modified power system transient stability performance as a consequence of the replacement of conventional power plants by wind generators. The selection of key variables is formulated as a non-linear optimization problem with weight factors as decision variables and is tackled by MVMO. A weight factor is assigned to each key variable candidate, and its value is considered to reflect the degree of influence of the key variable candidate on the splitting property and estimation accuracy of the DTs. The samples of the key variable candidates and the initialized weight factors are used to build the first group of DTs. Then, MVMO iteratively evolves the weight factors according to its special mapping function with minimizing DTs' estimation error. According to the final list of optimized weight factors, system operators can select a reduced set of variables with the largest weight factors as key variables, depending on the resulting accuracy of the DTs. Meanwhile, DTs built by using key variables are considered as the optimal performance trees for transient stability estimation. In this way, the selection of key variables and the development of DTs are made jointly and automatically, without the interference of the users of the DTs. Test results on the modified IEEE 9 bus system and a synthetic model of a real power system show that the proposed method can correctly identify the set of key variables related to wind turbine dynamics, as well as its ability to provide a reliable estimation of the transient stability margin.

This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.

This paper reports a general overview of current research on analysis and control of the power grid with grid scale PV-based power generations as well as of various consequences of grid scale integration of PV generation units into the power systems. Moreover, the history of PV renewable growth, deregulation of power system and issues related to grid-connected PV systems considering its contribution to various responsibilities like frequency control, virtual inertia capabilities and voltage regulation are discussed. Moreover, various outcomes of the high-penetrated grid with PV power plants such as power quality, active and reactive power control, protection, balancing and reliability under various loading conditions are reviewed and discussed.

This paper develops a model to study the inertial dynamics of single Type 4 Wind Turbine Generator (WTG)s that emulate the response of grid-friendly Virtual Synchronous Generator (VSG)s. In this vein, a simplified linearized model is developed for the WTG in the electromechanical time scale considering complete WTG dynamics to obtain/investigate the power System Frequency Response (SFR) characteristics. The objectives are to obtain virtual frequency motion equation, present the mechanism of inertia provision clearly, and feature physical interpretations of inertial frequency response. Accordingly, the available inertia constant and damping coefficient are estimated/expressed analytically in frequency-domain that synthetically embed the impacts of key control parameters as well as the initial operating points. It is realized that the available inertia constant and damping coefficient are adjustable and frequency dependent in contrast to that's manifested by a conventional synchronous generator. The developed virtual frequency motion equation is used in turn to establish/investigate the complete SFR model of a generic test power grid which features enormous wind power generation capacity factor. The theoretical analysis shows that the SFR attributes could get adjusted/enhanced by proper tuning of the WTG control parameters. Comprehensive and comparative time domain simulations are also performed in the MATLAB/Simulink platform. It is to verify the accuracy of the proposed models in the evaluation of SFR characteristics and authenticate the theoretical analysis.

Due to the power electronic converter based interface and maximum power control strategy, wind generators cannot directly respond to power system transients. This brings new challenges on power system transient stability. Taking wind turbine type 4 (WT4) as one example, this paper analyses its influence on transient stability with respect to locations, low voltage ride through parameters, wind power plant installation capacity and penetration levels. Based on the sensitivity analysis carried out for the influence of WT4, a supplementary transient stability control is proposed. The results on a 3-area system show that this supplementary control can improve transient stability of power systems with high penetration of wind power.

This paper presents a study based on sensitivity analysis to investigate the impact of fast frequency response (FFR) capability of type 4 wind turbine units on the overall frequency performance of interconnected power systems with low inertia. FFR capability is implemented by means of a control loop for inertia emulation (IE), which is superimposed on the active power control channel of the machine side converter of the wind turbine. All parameters of IE are assessed via sensitivity analysis to ascertain their influence on the system frequency performance within the time window of the frequency containment period. Both individual and collective response of the wind turbines are assessed to determine if the penetration level of wind power can be increased by customizing IE with the best parameter values found via sensitivity analysis. Numerical results, obtained by using a 3-area benchmark power system, illustrate the degree of increase in wind penetration level that can be achieved when choosing a suitable combination of wind turbine locations and considering suggested settings for IE.