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

A New Relaying Method for Third Zone Distance Relay Blocking During Power Swings

Due to the increasing complexity of modern bulk power systems, the power swing identification, blocking, and protection have become more challenging than they used to be. Among various transmission line protection methods, distance relays are the most commonly used type. One of the advantages of using distance relays is the zoned protection which provides redundancy. However, the additional redundancy comes with a problem that it increases the probability of incorrect operation. For example, the undesired operation of the third zone distance protection during power swing scenarios has been attributed as one of the major causes for creating large-scale blackouts. Some research works in the literature investigate proper identification of stable and unstable power swing conditions. Most research works dwell on identification of power swing conditions but do not address how the scheme could be used for blocking the third zone of distance relays during stable power swings. Also, the current power swing detection schemes are often very complex to implement for a relaying engineer or are not fast enough for blocking the third zone distance element. This research proposes a reliable and fast methodology for the third zone blocking (TZB) during power swings. The new mathematical formulations and derivations are based on sound time tested power system theory and are simpler to understand for a relaying engineer to implement this technique. The algorithm proposed in the research can prevent unnecessary tripping of distance relays during power swings. The algorithm also overcomes the shortcomings of the conventional power swing identification methods when applied for the third zone blocking. A first zero-crossing (FZC) concept is introduced as the criteria for identifying stable power swing or out-of-step phenomena. The analysis is based on system stability point of view and utilizes power-angle equations. The proposed algorithm could be applied at every discrete time interval or time step of a distance relay to detect power swing points. It could also be applied to any transmission line in the power system by finding an equivalent single machine infinite bus (SMIB) configuration individually for each line on a real-time basis, which is one of the primary advantages of the proposed method. In the thesis work, the proposed technique is first demonstrated using a simple single machine infinite bus system. The TZB algorithm is then tested using a modified Western Electricity Coordinating Council (WSCC) power system configuration using Power System Analysis Toolbox (PSAT) simulations. The code is written in MATLAB. The TZB method is then further analyzed using electromagnetic simulations with Real-Time Digital Simulator (RTDS) on WSCC system. The proposed method uses small time step simulations (50 μs) to take various aspects of power system complexity into consideration, such as different harmonics presents in the system, synchronous machine operation at different speeds, travelling wave representation of transmission lines instead of purely lumped parameter representation, etc. The investigations as mentioned above and the results show that the proposed TZB scheme is a straightforward and reliable technique, involving only a few calculation steps, and could be applied to any power system configuration. The main novelty of this technique is that it does not require a priori stability study to find the relay settings unlike conventional power swing identification or distance relay blocking techniques. The inputs to the relay are basic electrical quantities which could be easily measured locally on any transmission line. The local measurements would make the implementation of the proposed TZB simpler for relaying applications compared to Wide Area Measurement System (WAMS) based techniques. In a WAMS based relaying technique - the cost associated with the communication network, reliability of the communication network, impact of communication delay on relay, etc all become factors for actual industry use

Advanced fault diagnosis techniques and their role in preventing cascading blackouts

This dissertation studied new transmission line fault diagnosis approaches using new technologies and proposed a scheme to apply those techniques in preventing and mitigating cascading blackouts. The new fault diagnosis approaches are based on two time-domain techniques: neural network based, and synchronized sampling based. For a neural network based fault diagnosis approach, a specially designed fuzzy Adaptive Resonance Theory (ART) neural network algorithm was used. Several ap- plication issues were solved by coordinating multiple neural networks and improving the feature extraction method. A new boundary protection scheme was designed by using a wavelet transform and fuzzy ART neural network. By extracting the fault gen- erated high frequency signal, the new scheme can solve the difficulty of the traditional method to differentiate the internal faults from the external using one end transmis- sion line data only. The fault diagnosis based on synchronized sampling utilizes the Global Positioning System of satellites to synchronize data samples from the two ends of the transmission line. The effort has been made to extend the fault location scheme to a complete fault detection, classification and location scheme. Without an extra data requirement, the new approach enhances the functions of fault diagnosis and improves the performance. Two fault diagnosis techniques using neural network and synchronized sampling are combined as an integrated real time fault analysis tool to be used as a reference of traditional protective relay. They work with an event analysis tool based on event tree analysis (ETA) in a proposed local relay monitoring tool. An interactive monitoring and control scheme for preventing and mitigating cascading blackouts is proposed. The local relay monitoring tool was coordinated with the system-wide monitoring and control tool to enable a better understanding of the system disturbances. Case studies were presented to demonstrate the proposed scheme. An improved simulation software using MATLAB and EMTP/ATP was devel- oped to study the proposed fault diagnosis techniques. Comprehensive performance studies were implemented and the test results validated the enhanced performance of the proposed approaches over the traditional fault diagnosis performed by the transmission line distance relay

Power System Stability Analysis Using Wide Area Measurement System

Advances in wide area measurement systems have transformed power system operation from simple visualization, state estimation, and post-mortem analysis tools to real-time protection and control at the systems level. Transient disturbances (such as lightning strikes) exist only for a fraction of a second but create transient stability issues and often trigger cascading type failures. The most common practice to prevent instabilities is with local generator out-of-step protection. Unfortunately, out-of-step protection operation of generators may not be fast enough, and an instability may take down nearby generators and the rest of the system by the time the local generator relay operates. Hence, it is important to assess power system stability over transmission lines as soon as the transient instability is detected instead of relying on purely localized out-of-step protection in generators. This thesis proposes a synchrophasor-based out-of-step prediction methodology at the transmission line level using wide area measurements from optimal phasor measurement unit (PMU) locations in the interconnected system. Voltage and current measurements from wide area measurement systems (WAMS) are utilized to find the swing angles. The proposed scheme was used to predict the first swing out-of-step condition in a Western Systems Coordinating Council (WSCC) 9 bus power system. A coherency analysis was first performed in this multi-machine system to determine the two coherent groups of generators. The coherent generator groups were then represented with a two-machine equivalent system, and the synchrophasor-based out-of-step prediction algorithm then applied to the reduced equivalent system. The coherency among the group of generators was determined within 100 ms for the contingency scenarios tested. The proposed technique is able to predict the instability 141.66 to 408.33 ms before the system actually reaches out-of-step conditions. The power swing trajectory is either a steady-state trajectory, monotonically increasing type (when the system becomes unstable), or oscillatory type (under stable conditions). Un- der large disturbance conditions, the swing could also become non-stationary. The mean and variance of the signal is not constant when it is monotonically increasing or non-stationary. An autoregressive integrated (ARI) approach was developed in this thesis, with differentiation of two successive samples done to make the mean and variance constant and facilitate time series prediction of the swing curve. Electromagnetic transient simulations with a real-time digital simulator (RTDS) were used to test the accuracy of the proposed algorithm with respect to predicting transient in- stability conditions. The studies show that the proposed method is computationally efficient and accurate for larger power systems. The proposed technique was also compared with a conventional two blinder technique and swing center voltage method. The proposed method was also implemented with actual PMU measurements from a relay (General Electric (GE) N60 relay). The testing was carried out with an interface between the N60 relay and the RTDS. The WSCC 9 bus system was modeled in the simulator and the analog time signals from the optimal location in the network communicated to the N60 relay. The synchrophasor data from the PMUs in the N60 were used to back-calculate the rotor angles of the generators in the system. Once the coherency was established, the swing curves for the coherent group of generators were found from time series prediction (ARI model). The test results with the actual PMUs match quite well with the results obtained from virtual PMU-based testing in the RTDS. The calculation times for the time series prediction are also very small. This thesis also discusses a novel out-of-step detection technique that was investigated in the course of this work for an IEEE Power Systems Relaying Committee J-5 Working Group document using real-time measurements of generator accelerating power. Using the derivative or second derivative of a measurement variable significantly amplifies the noise term and has limited the actual application of some methods in the literature, such as local measurements of voltage or voltage deviations at generator terminals. Another problem with the voltage based methods is taking an average over a period; the intermediate values cancel out and, as a result, just the first and last sample values are used to find the speed. This effectively means that the sample values in between are not used. The first solution proposed to overcome this is a polynomial fitting of the points of the calculated derivative points (to calculate speed). The second solution is the integral of the accelerating power method (this eliminates taking a derivative altogether). This technique shows the direct relationship of electrical power deviation to rotor acceleration and the integral of accelerating power to generator speed deviation. The accelerating power changes are straightforward to measure and the values obtained are more stable during transient conditions. A single machine infinite bus (SMIB) system was used for the purpose of verifying the proposed local measurement based method

A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies

Modeling protective relays is crucial for performing accurate stability studies as they play a critical role in defining the dynamic responses of power systems during disturbances. Nevertheless, due to the current limitations of stability software and the challenges of keeping track of the changes in the settings information of thousands of protective relays, modeling all the protective relays in bulk power systems is a challenging task. Distance relays are among the critical protection schemes, which are not properly modeled in current practices of stability studies. This paper proposes a machine learning-based method that uses the results of early-terminated stability studies to identify the critical distance relays required to be modeled in those studies. The algorithm used is the random forest (RF) classifier. GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The model is trained and tested on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer peak load under different operating conditions and topologies of the system. The results show the great performance of the method in identifying the critical distance relays. The results also show that only modeling the identified critical distance relays suffices to perform accurate stability studies.Comment: 10 pages, 5 figure

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Big Data Analytics in Smart Grids for Renewable Energy Networks: Systematic Review of Information and Communication Technology Tools

El desarrollo industrial y económico de los países industrializados, a partir del siglo XIX, ha ido de la mano del desarrollo de la electricidad, del motor de combustión interna, de los ordenadores, de Internet, de la utilización de datos y del uso intensivo del conocimiento centrado en la ciencia y la tecnología. La mayoría de las fuentes de energía convencionales han demostrado ser finitas y agotables. A su vez, las diferentes actividades de producción de bienes y servicios que utilizan combustibles fósiles y energía convencional, han aumentado significativamente la contaminación del medio ambiente, y con ello, han contribuido al calentamiento global. El objetivo de este trabajo fue realizar una aproximación teórica a las tecnologías de análisis de datos e inteligencia de negocio aplicadas a las redes de sistemas eléctricos inteligentes con energías renovables. Para este trabajo se realizó una revisión bibliométrica y bibliográfica sobre Big Data Analytics, herramientas TIC de la industria 4.0 y Business intelligence en diferentes bases de datos disponibles en el dominio público. Los resultados del análisis indican la importancia del uso de la analítica de datos y la inteligencia de negocio en la gestión de las empresas energéticas. El trabajo concluye señalando cómo se está aplicando la inteligencia de negocio y la analítica de datos en ejemplos concretos de empresas energéticas y su creciente importancia en la toma de decisiones estratégicas y operativasThe industrial and economic development of the industrialized countries, from the nineteenth century, has gone hand in hand with the development of electricity, the internal combustion engine, computers, the Internet, data use and the intensive use of knowledge focused on science and the technology. Most conventional energy sources have proven to be finite and exhaustible. In turn, the different production activities of goods and services using fossil fuels and conventional energy, have significantly increased the pollution of the environment, and with it, contributed to global warming. The objective of this work was to carry out a theoretical approach to data analytics and business intelligence technologies applied to smart electrical-system networks with renewable energies. For this paper, a bibliometric and bibliographic review about Big Data Analytics, ICT tools of industry 4.0 and Business intelligence was carried out in different databases available in the public domain. The results of the analysis indicate the importance of the use of data analytics and business intelligence in the management of energy companies. The paper concludes by pointing out how business intelligence and data analytics are being applied in specific examples of energy companies and their growing importance in strategic and operational decision makinghttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503https://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eshttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961https://orcid.org/0000-0003-1166-198

Big data analytics in smart grids: a review

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