Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Measurements, Models, Systems and Design

531 s.

A coordinated voltage regulation algorithm of a power distribution grid with multiple photovoltaic distributed generators based on active power curtailment and on-line tap changer

The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.Peer ReviewedPostprint (published version

Power quality enhancement using power balance theory based DSTATCOM

The DSTATCOM (Distributed Static Compensator) is used for current harmonic mitigation, Power Factor Correction (PFC), reactive power compensation, load balancing and neutral current compensation in the Power Distribution System (PDS). In this paper, the power balance theory based DSTATCOM is used for power quality enhancement like current harmonic mitigation, power factor correction (PFC), reactive power compensation, load balancing and neural current compensation and load balancing. A non-isolated star/delta transformer is to reduce dc-link voltage v_(dc) of Voltage Source Converter (VSC) and neutral current compensation. The reference source currents can be extracted quickly by using proposed power balance theory. The proposed power balance theory based DSTATCOM is modeled and simulated using MATLAB/SIMULINK under PFC and ZVR (Zero Voltage Regulation) operations

Protection Challenges of Distributed Energy Resources Integration In Power Systems

It is a century that electrical power system are the main source of energy for the societies and industries. Most parts of these infrastructures are built long time ago. There are plenty of high rating high voltage equipment which are designed and manufactured in mid-20th and are currently operating in United States’ power network. These assets are capable to do what they are doing now. However, the issue rises with the recent trend, i.e. DERs integration, causing fundamental changes in electrical power systems and violating traditional network design basis in various ways. Recently, there have been a steep rise in demands for Distributed Energy Resources (DERs) integration. There are various incentives for demand in such integrations and employment of distributed and renewable energy resources. However, it violates the most fundamental assumption in power system traditional designs. That is the power flows from the generation (upstream) toward the load locations (downstream). Currently operating power systems are designed based on this assumption and consequently their equipment ratings, operational details, protection schemes, and protections settings. Violating these designs and operational settings leads toward reducing the power reliability and increasing outages, which are opposite of the DERs integration goals. The DERs integration and its consequences happen in both transmission and distribution levels. Both of these networks effects of DERs integration are discussed in this dissertation. The transmission level issues are explained in brief and more analytical approach while the transmission network challenges are provided in details using both field data and simulation results. It is worth mentioning that DERs integration is aligned with the goal to lead toward a smart grid. This can be considered the most fundamental network reconfiguration that has ever experienced and requires various preparations. Both long term and short term solutions are proposed for the explained challenges and corresponding results are provided to illustrate the effectiveness of the proposed solutions. The author believes that developing and considering short term solutions can make the transition period toward reaching the smart grid possible. Meanwhile, long term approaches should also be planned for the final smart grid development and operation details

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.Comment: Accepted by Applied Science

Investigation into the effect of wind power based embedded generators on distribution networks.

Wind turbine-generators are usually integrated into utilities' electrical networks at distribution voltage levels, and they are commonly known as "Embedded Generators" (EGs). Recently, it has been reported that the integration of wind power-based embedded generators (WPBEGs) into distribution networks could cause maloperation of automatic voltage control (AVC) relays. Further investigation is therefore required to improve the performance of AVC relays in the presence of EGs. On the other hand, the dynamic effects of WPBEGs on distribution networks (DNs) have been investigated for many years, but no attempt has been made to evaluate the effects of WPBEGs on the "Critical Clearing Time" (CCT) of faults on load feeders emanating from the substation where EGs are connected to the network. Based on these findings, the work conducted and reported in this thesis covers two main aspects. The first aspect is related to the effect of EGs on the operation of AVC relays, including the compensation of voltage drop along distribution feeders. This is preceded by an introduction to the operating principles of conventional AVC relays. A new model of an AVC relay based on the application of Artificial Neural Networks (ANN) is then presented. The model is designed and trained to calculate the AVC voltage that is used to initiate the operation of the tap-changer of an appropriate transformer as conditions necessitate. In the process of the development of an ANN-based relay, a power flow program has been specially designed to generate training files using FORTRAN. The second aspect reported in this thesis deals with the investigation of the effect of WPBEGs on the CCT of faults on load feeders. It has been concluded that CCT of faults, which is required to maintain the stability of WPBEGs, can be several times less than that of the operating time of conventional protection schemes usually used on distribution feeders. The results obtained from the investigation related to both aspects are presented and discussed. In summary, this thesis reports on the outcome of the investigation related to the design of an ANN based AVC relay capable of accommodating EGs and the effect of the dynamic behaviour of EGs on the CCT of faults on load feeders

Controlling Techniques for STATCOM using Artificial Intelligence

The static synchronous compensator (STATCOM) is a power electronic converter designed to be shunt-connected with the grid to compensate for reactive power. Although they were originally proposed to increase the stability margin and transmission capability of electrical power systems, there are many papers where these compensators are connected to distribution networks for voltage control and power factor compensation. In these applications, they are commonly called distribution static synchronous compensator (DSTATCOM). In this paper we have focussed on STATCOM and the controlling techniques which are based on artificial intelligence