Power system stability scanning and security assessment using machine learning

Future grids planning requires a major departure from conventional power system planning, where only a handful of the most critical scenarios is analyzed. To account for a wide range of possible future evolutions, scenario analysis has been proposed in many industries. As opposed to the conventional power system planning, where the aim is to find an optimal transmission and/or generation expansion plan for an existing grid, the aim in future grids scenario analysis is to analyze possible evolution pathways to inform power system planning and policy making. Therefore, future grids’ planning may involve large amount of scenarios and the existing planning tools may no longer suitable. Other than the raised future grids’ planning issues, operation of future grids using conventional tools is also challenged by the new features of future grids such as intermittent generation, demand response and fast responding power electronic plants which lead to much more diverse operation conditions compared to the existing networks. Among all operation issues, monitoring stability as well as security of a power system and action with deliberated preventive or remedial adjustment is of vital important. On- line Dynamic Security Assessment (DSA) can evaluate security of a power system almost instantly when current or imminent operation conditions are supplied. The focus of this dissertation are, for future grid planning, to develop a framework using Machine Learning (ML) to effectively assess the security of future grids by analyzing a large amount of the scenarios; for future grids operation, to propose approaches to address technique issues brought by future grids’ diverse operation conditions using ML techniques. Unsupervised learning, supervised learning and semi-supervised learning techniques are utilized in a set of proposed planning and operation security assessment tools

Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments

Context-based energy disaggregation in smart homes

In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments

Multi-Objective Drive-Cycle Based Design Optimization of Permanent Magnet Synchronous Machines

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this thesis, a vehicle dynamics model for a direct-drive machine and its associated vehicle parameters is implemented for the urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, power, and energy. K-means clustering and Gaussian mixture modeling (GMM) are two clustering techniques used to reduce the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle. These methods offer high computational efficiency and low computational time cost while optimizing an electric machine. Differential evolution (DE) is employed to optimize the baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM). A computationally efficient finite element analysis (CEFEA) technique is developed to evaluate the machine at the representative drive-cycle points elicited from the clustering approaches. In addition, a steady-state thermal model is established to assess the electric motor temperature variation between optimization design candidates. In an alternative application, the drive-cycle cluster points are utilized for a computationally efficient drive-cycle system simulation that examines the effects of inverter time harmonics on motor performance. The motor is parameterized and modeled in a PSIM motor-inverter simulation that determines the current excitation harmonics that are injected into the machine during drive-cycle operation. These current excitations are inserted into the finite element analysis motor simulation for accurate analysis of the harmonic effects. The analysis summarizes the benefits of high-frequency devices such as gallium nitride (GaN) in comparison to insulated gate bipolar transistors (IGBT) in terms of torque ripple and motor efficiency on a drive-cycle

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

Context-based energy disaggregation in smart homes

In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments

Review of Clustering Methods for Slow Coherency-Based Generator Grouping

Slow coherency is one of the most relevant concepts used in power systems dynamics to group generators that exhibit similar response to disturbances. Among the approaches developed for generator grouping based on slow coherency, clustering algorithms play a significant role. This paper reviews the clustering algorithms applied in model-based and data-driven approaches, highlighting the metrics used, the feature selection, the types of algorithms and the comparison among the results obtained considering simulated or measured data

ON-LINE TRANSIENT STABILITY STUDIES INCORPORATING WIND POWER

Transient stability is a major concern in power system security and reliability because it is the most common type of instability and its impacts can cause greatest economic losses. For enhancing the energy security, it requires the power system operation to be evaluated during both the planning and the operation stage. Many online/offline transient stability assessment techniques have already been developed for this purpose. However, due to the increase in energy demand, the modern power system has grown to a very sophisticated and large system for which extent transient stability assessment methods may not be able to handle. In addition, the new published regulation rules and new concepts such as the smart grid have also pushed the requirement for transient stability assessment to a higher level. Thus, this dissertation is intended to study large scale power system transient stability. It starts from establishing an analytical approach for power system transient stability assessment. Based on the results, the disadvantages of traditional concepts used in transient stability assessment have been discussed. In order to overcome the difficulties encountered by classical approaches, a new technique for estimating the generator rotor angle difference in multi-machine power system is developed. It is more practical and has been applied to study the impact of wind power generation on power system transient stability afterwards. Since recently there is a significant increase in the importance of renewable energy and its related optimizations in power systems, the final goal of this dissertation focuses on the power system optimal power flow technique with wind power penetration and transient stability constrains. For making results more convincible, the South Carolina offshore wind speed data is used as the availability of wind power. An approach for maintaining the power system economic operation within the security range has been given at the end of this dissertation