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

Benefits of Waveform Relaxation Method and Output Space Mapping for the Optimization of Multirate Systems

We present an optimization problem that requires to model a multirate system, composed of subsystems with different time constants. We use waveform relaxation method in order to simulate such a system. But computation time can be penalizing in an optimization context. Thus we apply output space mapping which uses several models of the system to accelerate optimization. Waveform relaxation method is one of the models used in output space mapping

Hardware Prototype for a Multi Agent Grid Management System

There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect

Transformer Faults Classification Based on Convolution Neural Network

This paper studies the latest advances made in Deep Learning (DL) methods utilized for transformer inrush and fault currents classification. Inrush and fault currents at different operating conditions, initial flux and fault type are simulated. This paper presents a technique for the classification of power transformer faults which is based on a DL method called convolutional neural network (CNN) and compares it with traditional artificial neural network (ANN) and other techniques. The inrush and fault current signals of the transformer are simulated within MATLAB by using Fourier analyzers that provides the 2nd harmonic signal. The 2nd harmonic peak and variance statistic values of input signals of the three phases of transformer are used at different operating conditions. The resulted values are aggregated into a dataset to be used as an input for the CNN model, then training and testing the CNN model is performed. Consequently, it is obvious that the CNN algorithm achieves a better performance compared to other algorithms. This study helps with easy discrimination between normal signals and faulty signals and to determine the type of the fault to clear it easily

Modeling and Analysis of Power Processing Systems

The feasibility of formulating a methodology for the modeling and analysis of aerospace electrical power processing systems is investigated. It is shown that a digital computer may be used in an interactive mode for the design, modeling, analysis, and comparison of power processing systems

Data-driven Protection of Transformers, Phase Angle Regulators, and Transmission Lines in Interconnected Power Systems

This dissertation highlights the growing interest in and adoption of machine learning approaches for fault detection in modern electric power grids. Once a fault has occurred, it must be identified quickly and a variety of preventative steps must be taken to remove or insulate it. As a result, detecting, locating, and classifying faults early and accurately can improve safety and dependability while reducing downtime and hardware damage. Machine learning-based solutions and tools to carry out effective data processing and analysis to aid power system operations and decision-making are becoming preeminent with better system condition awareness and data availability. Power transformers, Phase Shift Transformers or Phase Angle Regulators, and transmission lines are critical components in power systems, and ensuring their safety is a primary issue. Differential relays are commonly employed to protect transformers, whereas distance relays are utilized to protect transmission lines. Magnetizing inrush, overexcitation, and current transformer saturation make transformer protection a challenge. Furthermore, non-standard phase shift, series core saturation, low turn-to-turn, and turn-to-ground fault currents are non-traditional problems associated with Phase Angle Regulators. Faults during symmetrical power swings and unstable power swings may cause mal-operation of distance relays, and unintentional and uncontrolled islanding. The distance relays also mal-operate for transmission lines connected to type-3 wind farms. The conventional protection techniques would no longer be adequate to address the above-mentioned challenges due to their limitations in handling and analyzing the massive amount of data, limited generalizability of conventional models, incapability to model non-linear systems, etc. These limitations of conventional differential and distance protection methods bring forward the motivation of using machine learning techniques in addressing various protection challenges. The power transformers and Phase Angle Regulators are modeled to simulate and analyze the transients accurately. Appropriate time and frequency domain features are selected using different selection algorithms to train the machine learning algorithms. The boosting algorithms outperformed the other classifiers for detection of faults with balanced accuracies of above 99% and computational time of about one and a half cycles. The case studies on transmission lines show that the developed methods distinguish power swings and faults, and determine the correct fault zone. The proposed data-driven protection algorithms can work together with conventional differential and distance relays and offer supervisory control over their operation and thus improve the dependability and security of protection systems

Protective Relaying Student Laboratory

Facing a rapidly-changing power industry, the electrical engineering department at Cal Poly San Luis Obispo proposed Advanced Power Systems Initiatives to better prepare its students for entering the power industry. These initiatives call for the creation of a new laboratory curriculum that uses microprocessor-based relays to reinforce the fundamental concepts of power system protection. This paper summarizes a laboratory system fit for this task and presents a set of proposed laboratory experiments to establish a new laboratory course at Cal Poly. The experiments expose students to the capabilities of industry-standard microprocessor-based relays through hands-on procedures that demonstrate common power system protection schemes. Relays studied in this project support transformer, transmission line, and induction motor protection

Modeling relays for power system protection studies

Numerical relays are the result of the application of microprocessor technology in relay industry. Numerical relays have the ability to communicate with its peers, are economical and are easy to operate, adjust and repair. Modeling of digital and numerical relays is important to adjust and settle protection equipment in electrical facilities and to train protection personnel. Designing of numerical relays is employed to produce new prototypes and protection algorithms. Computer models of numerical relays for the study of protection systems are greatly enhanced when working along with an electromagnetic transient program (emtp). A literature survey has revealed that previous modeling techniques presented a lack of automation in the generation of relay models, or show high complexity in linking the numerical relay models with the power system modeled in the emtp. This thesis describes a new approach of modeling and designing of numerical relays. The proposed methodology employs a Visual C++-based program (PLSA) to obtain from the user the specifications of the relay to be designed, and to process this information to generate the FORTRAN code that represents the functional blocks of the relay. This generated code is incorporated in a PSCAD/EMTDC case using a resource called component, which facilitates the creation of user-custom models in PSCAD/EMTDC. Convenient electrical and logical signals are connected to the inputs and outputs of the PSCAD/EMTDC component. Further additions of digital relay models into the PSCAD/EMTDC case constitute the protection system model. The thesis describes a procedure for designing distance and differential relay models, but the methodology may be extended to design models of other relay elements. A number of protection system studies were performed with the structure created with the proposed methodology. Adjustment of distance and differential relays were studied. Relay performance under CT saturation and the effects of the removal of anti-aliasing analog filter were investigated. Local and remote backup distance protection of transmission lines was simulated. The adjustment of differential protection of power transformer to overcome the effects of inrush current was performed. Power transformer differential protection responses to internal and external faults were considered. Additionally, a set of tests were performed to investigate the consistency of the relay models generated with the proposed methodology. The results showed that the numerical relay models respond satisfactorily according with the expected results of the tests