Matching Items (30)
Description
The Java-Bali power system is the biggest power system in Indonesia. On September 5th, 2018 at 11:26 AM, a region in the East Java-Bali subsystem suffered a blackout due to a single line to ground fault that disrupted the stability of the interconnected system and caused cascaded tripping.
This thesis presents the results of an evaluation of the dynamic performance of the East Java-Bali subsystem. It involves the static and dynamic simulations of the sequence of events that led to the East Java Bali subsystem blackout, especially the impact of the loss of a set of 500 kV transmission lines, which in reality was suspected to be the main cause.
The basic calculations related to power system state and familiarization with PSS/E (a commercial power system analysis software package) are first demonstrated. A simple 3-bus system test is taken as an example. The steady state characteristics of the active and reactive power injection, voltage and phase angle are calculated manually and compared to the PSS/E simulation results. As for the dynamic characteristics, short circuit current, electrical and mechanical power, rotor angle, and fault clearing time are determined by observing the plots of the simulation results. Based on understanding of the PSS/E modeling and simulation, the configuration, generation, and loading of the simplified East Java-Bali subsystem is evaluated. The generators (including the excitation system and governor) and transmission lines parameters are updated, as the reference model for the study. The model is validated by the actual data (active power flow) before the fault. Single line to ground fault and loss of generation disturbances were simulated to observe the stability of the system.
The analysis of the blackout is conducted through the simulation results based on all relevant documentation (such as fault report and sequence of events). With respect to the sequence of events (a single line to ground fault on the 500kV transmission lines, overload on 150kV transmission lines and tripping of power plants), several simulations of the East Java-Bali subsystem operations provided in the official blackout report are evaluated. Finally, the undervoltage load shedding strategy is evaluated and proposed as a solution to mitigate the blackout in the East Java-Bali subsystem.
The simulations reveal some interesting results regarding the operational characteristics of the East Java-Bali subsystem before the disturbances and during the cascaded tripping.
This thesis presents the results of an evaluation of the dynamic performance of the East Java-Bali subsystem. It involves the static and dynamic simulations of the sequence of events that led to the East Java Bali subsystem blackout, especially the impact of the loss of a set of 500 kV transmission lines, which in reality was suspected to be the main cause.
The basic calculations related to power system state and familiarization with PSS/E (a commercial power system analysis software package) are first demonstrated. A simple 3-bus system test is taken as an example. The steady state characteristics of the active and reactive power injection, voltage and phase angle are calculated manually and compared to the PSS/E simulation results. As for the dynamic characteristics, short circuit current, electrical and mechanical power, rotor angle, and fault clearing time are determined by observing the plots of the simulation results. Based on understanding of the PSS/E modeling and simulation, the configuration, generation, and loading of the simplified East Java-Bali subsystem is evaluated. The generators (including the excitation system and governor) and transmission lines parameters are updated, as the reference model for the study. The model is validated by the actual data (active power flow) before the fault. Single line to ground fault and loss of generation disturbances were simulated to observe the stability of the system.
The analysis of the blackout is conducted through the simulation results based on all relevant documentation (such as fault report and sequence of events). With respect to the sequence of events (a single line to ground fault on the 500kV transmission lines, overload on 150kV transmission lines and tripping of power plants), several simulations of the East Java-Bali subsystem operations provided in the official blackout report are evaluated. Finally, the undervoltage load shedding strategy is evaluated and proposed as a solution to mitigate the blackout in the East Java-Bali subsystem.
The simulations reveal some interesting results regarding the operational characteristics of the East Java-Bali subsystem before the disturbances and during the cascaded tripping.
ContributorsRoekman, Taufan Marviansha (Author) / Vittal, Vijay (Thesis director) / Pal, Anamitra (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Three-Phase brushless DC motors (BLDC) have become increasingly popular in many fields including industrial controls and remote-control hobby toys. They offer many advantages over their brushed counterparts such as smaller size, longer service life, and increased efficiency; however, one drawback is that commutation must be handled electrically using a controller rather than by a mechanical commutator. Rotor position must be estimated in order to accurately commutate the motor, this is calculated either by sensors (sensored) or by measuring the generated Back-Electromotive Force (sensorless). There are two primary methods of brushless DC motor commutation, trapezoidal and sinusoidal. Both methods have advantages and disadvantages, as well as unique sets of rotor position estimation strategies. This paper will discuss in detail the development of a novel motor control algorithm that employs one method of sensorless trapezoidal control of BLDC motors where the BEMF is integrated after a zero-crossing event, the various challenges associated with direct BEMF measurement, and demonstrate a practical implementation of the new algorithm. Using a robust, high frequency sampling scheme and on-the-fly detection strategies, this new algorithm overcomes many of the shortcomings of similar control algorithms currently available on the market. As a result, this new algorithm provides even more robust control over BLDC motors, increased efficiency, and improved dynamic performance compared to its counterparts while simultaneously requiring little to no additional hardware in practical implementations. Topics investigated include BLDC motors, sensored and sensorless rotor estimation, PWM strategies, terminal voltage sensing, third harmonic voltage sensing and integration, sample timing, switching noise, and current recirculation.
ContributorsYin, Kai (Author) / Chickamenahalli, Shamala (Thesis director) / Holbert, Keith (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are one of the main factors driving the behavior of a power system and they depend on external phenomena which are not captured by traditional simulation tools. Thus, accurate models that capture the fundamental characteristics of time-series load dataare necessary.
In the first part of this dissertation, an example of successful application of machine learning algorithms that leverage load data is presented. Prior work has shown that power systems energy management systems are vulnerable to false data injection attacks against state estimation. Here, a data-driven approach for the detection and localization of such attacks is proposed. The detector uses historical data to learn the normal behavior of the loads in a system and subsequently identify if any of the real-time observed measurements are being manipulated by an attacker.
The second part of this work focuses on the design of generative models for time-series load data. Two separate techniques are used to learn load behaviors from real datasets and exploiting them to generate realistic synthetic data. The first approach is based on principal component analysis (PCA), which is used to extract common temporal patterns from real data. The second method leverages conditional generative adversarial networks (cGANs) and it overcomes the limitations of the PCA-based model while providing greater and more nuanced control on the generation of specific
types of load profiles. Finally, these two classes of models are combined in a multi-resolution generative scheme which is capable of producing any amount of time-series load data at any sampling resolution, for lengths ranging from a few seconds to years.
ContributorsPinceti, Andrea (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pal, Anamitra (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Low frequency oscillations (LFOs) are recognized as one of the most challenging problems in electric grids as they limit power transfer capability and can result in system instability. In recent years, the deployment of phasor measurement units (PMUs) has increased the accessibility to time-synchronized wide-area measurements, which has, in turn, enabledthe effective detection and control of the oscillatory modes of the power system. This work assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) of high voltage DC (HVDC) lines, for damping electromechanical oscillations in a modern power system. The improved damping is achieved by designing different types of coordinated wide-area damping controllers (CWADC) that employ partial state-feedback. The first design methodology uses a linear matrix inequality (LMI)-based mixed H2/Hinfty control that is robust for multiple operating scenarios. To counteract the negative impact of communication failure or missing PMU measurements on the designed control, a scheme to identify the alternate set of feedback signals is proposed. Additionally, the impact of delays on the performance of the control design is investigated.
The second approach is motivated by the increasing popularity of artificial intelligence (AI) in enhancing the performance of interconnected power systems. Two different wide-area coordinated control schemes are developed using deep neural networks (DNNs) and deep reinforcement learning (DRL), while accounting for the uncertainties present in the power system. The DNN-CWADC learns to make control decisions using supervised learning; the training dataset consisting of polytopic controllers designed with the help of LMI-based mixed H2/Hinfty optimization. The DRL-CWADC learns to adapt to the system uncertainties based on its continuous interaction with the power system environment by employing
an advanced version of the state-of-the-art deep deterministic policy gradient (DDPG) algorithm referred to as bounded exploratory control-based DDPG (BEC-DDPG). The studies performed on a 29 machine, 127 bus equivalent model of theWestern Electricity Coordinating Council (WECC) system-embedded with different types of damping controls have demonstrated the effectiveness and robustness of the proposed CWADCs.
ContributorsGupta, Pooja (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Thesis advisor) / Zhang, Junshan (Committee member) / Hedmnan, Mojdeh (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2021
Description
Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI).
Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets.
The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.
ContributorsSen Biswas, Reetam (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Committee member) / Undrill, John (Committee member) / Wu, Meng (Committee member) / Zhang, Yingchen (Committee member) / Arizona State University (Publisher)
Created2021
Description
With the continued increase in the amount of renewable generation in the formof distributed energy resources, reliability planning has progressively become a more
challenging task for the modern power system. This is because with higher penetration
of renewable generation, the system has to bear a higher degree of variability
and uncertainty. One way to address this problem is by generating realistic scenarios
that complement and supplement actual system conditions. This thesis presents
a methodology to create such correlated synthetic scenarios for load and renewable
generation using machine learning.
Machine learning algorithms need to have ample amounts of data available to
them for training purposes. However, real-world datasets are often skewed in the
distribution of the different events in the sample space. Data augmentation and
scenario generation techniques are often utilized to complement the datasets with
additional samples or by filling in missing data points. Datasets pertaining to the
electric power system are especially prone to having very few samples for certain
events, such as abnormal operating conditions, as they are not very common in an
actual power system. A recurrent generative adversarial network (GAN) model is
presented in this thesis to generate solar and load scenarios in a correlated manner
using an actual dataset obtained from a power utility located in the U.S. Southwest.
The generated solar and load profiles are verified both statistically and by implementation
on a simulated test system, and the performance of correlated scenario
generation vs. uncorrelated scenario generation is investigated. Given the interconnected
relationships between the variables of the dataset, it is observed that correlated
scenario generation results in more realistic synthetic scenarios, particularly for abnormal
system conditions. When combined with actual but scarce abnormal conditions,
the augmented dataset of system conditions provides a better platform for performing contingency studies for a more thorough reliability planning.
The proposed scenario generation method is scalable and can be modified to work
with different time-series datasets. Moreover, when the model is trained in a conditional
manner, it can be used to synthesise any number of scenarios for the different
events present in a given dataset. In summary, this thesis explores scenario generation
using a recurrent conditional GAN and investigates the benefits of correlated
generation compared to uncorrelated synthesis of profiles for the reliability planning
problem of power systems.
ContributorsBilal, Muhammad (Author) / Pal, Anamitra (Thesis advisor) / Holbert, Keith (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2022
Description
Renewable energy and carbon reduction policies are creating new challenges for electricity markets. To achieve carbon-free goals, large-scale battery energy storage systems (BESSs) are necessary to ensure grid reliability and flexibility. The impact of BESSs on market and grid operation, as well as the optimal portfolio across the energy and ancillary services markets, must be analyzed to guide their operation. At the same time, the expansion of renewable and storage resources and the adoption of carbon reduction policies have introduced new complexities to the bidding behavior of market participants, which cannot be easily described by cost-based bidding objectives. In response to these challenges, this dissertation aims to achieve two research objectives: (I) enable BESS participation in energy and ancillary services markets under uncertainties, considering the battery's degradation cost; (II) identify robust bidding objectives for electricity market participants based on their historical bidding data. Three optimization frameworks are proposed in Part I to model a BESS as a price-maker in energy markets, evaluating its impact on market outcomes. The preliminary framework models automatic generation control signals, while the detailed framework proposes a participation factor for dispatching AGC signals and accounts for battery degradation costs. The stochastic framework models spinning reserve deployment with uncertainty and propose an optimization-based approximation method based on reinforcement learning. Case studies on proposed frameworks validate operational models for Battery Energy Storage Systems (BESS) and markets, showing the accuracy and efficiency of the approximation approach. Key findings include that accurate degradation cost modeling is essential, and participation in ancillary services markets is more profitable. Part II proposes a data-driven approach using Adversarial Inverse Reinforcement Learning to identify robust bidding objectives for electricity market participants. It introduces a tailored reinforcement learning model for bidding objective identification without data discretization, and a special policy structure compliant with multi-segment bidding rules. Two approaches are suggested for electricity market environment modeling in RL/IRL problems, ensuring the robustness of the identified bidding objective. Three case studies validated the accuracy and robustness of the proposed bidding objective identification method in various application scenarios.
ContributorsKhalilisenobari, Reza (Author) / Wu, Meng (Thesis advisor) / Vitall, Vijay (Committee member) / Pal, Anamitra (Committee member) / Khorsand, Mojdeh (Committee member) / Arizona State University (Publisher)
Created2023
Description
Event identification is increasingly recognized as crucial for enhancing the reliability, security, and stability of the electric power system. With the growing deployment of Phasor Measurement Units (PMUs) and advancements in data science, there are promising opportunities to explore data-driven event identification via machine learning classification techniques. This dissertation explores the potential of data-driven event identification through machine learning classification techniques. In the first part of this dissertation, using measurements from multiple PMUs, I propose to identify events by extracting features based on modal dynamics. I combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types.Using the obtained set of features, I investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA.
My results indicate that the proposed framework is promising for identifying the two types of events in the supervised setting. In the second part of the dissertation, I use semi-supervised learning techniques, which make use of both labeled and unlabeled samples.I evaluate three categories of classical semi-supervised approaches: (i) self-training, (ii) transductive support vector machines (TSVM), and (iii) graph-based label spreading (LS) method. In particular, I focus on the identification of four event classes i.e., load loss, generation loss, line trip, and bus fault. I have developed and publicly shared a comprehensive Event Identification package which consists of three aspects: data generation, feature extraction, and event identification with limited labels using semi-supervised methodologies. Using this package, I generate eventful PMU data for the South Carolina 500-Bus synthetic network. My evaluation confirms that the integration of additional unlabeled samples and the utilization of LS for pseudo labeling surpasses the outcomes achieved by the self-training and TSVM approaches. Moreover, the LS algorithm consistently enhances the performance of all classifiers more robustly.
ContributorsTaghipourbazargani, Nima (Author) / Kosut, Oliver (Thesis advisor) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023
Description
This report studies the optimal mechanisms for the vertically integrated utility to dispatch and incentivize the third-party demand response (DR) providers in its territory. A framework is proposed, with three-layer coupled Stackelberg and simultaneous games, to study the interactions and competitions among the pro t-seeking process of the utility, the third-party DR providers, and the individual end users (EUs) in the DR programs. Two coupled single-leader-multiple-followers Stackelberg games with a three-layer structure are proposed to capture the interactions among the utility (modeled in the upper layer), the third-party DR providers (modeled in the middle layer), and the EUs in each DR program (modeled in the lower layer). The competitions among the EUs in each DR program is captured through a non-cooperative simultaneous game. An inconvenience cost function is proposed to model the DR provision willingness and capacity of different EUs. The Stackelberg game between the middle-layer DR provider and the lower-layer EUs is solved by converting the original bi-level programming to a single level programming. This converted single level programming is embedded in an iterative algorithm toward solving the entire coupled games framework. Case studies are performed on IEEE 34-bus and IEEE69-bus test systems to illustrate the application of the proposed framework.
ContributorsSajjadi, Sayyid Mohssen (Author) / Wu, Meng (Thesis advisor) / Hedman, Mojdeh (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2023
Description
The past few years have witnessed a significant growth of distributed energy resources (DERs) in power systems at the customer level. Such growth challenges the traditional centralized model of conventional synchronous generation, making a transition to a decentralized network with a significant increase of DERs. This decentralized network requires a paradigm change in modeling distribution systems in more detail to maintain the reliability and efficiency while accommodating a high level of DERs. Accurate models of distribution feeders, including the secondary network, loads, and DER components must be developed and validated for system planning and operation and to examine the distribution system performance. In this work, a detailed model of an actual feeder with high penetration of DERs from an electrical utility in Arizona is developed. For the primary circuit, distribution transformers, and cables are modeled. For the secondary circuit, actual conductors to each house, as well as loads and photovoltaic (PV) units at each premise are represented. An automated tool for secondary network topology construction for load feeder topology assignation is developed. The automated tool provides a more accurate feeder topology for power flow calculation purposes. The input data for this tool consists of parcel geographic information system (GIS) delimitation data, and utility secondary feeder topology database. Additionally, a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements is presented. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification.
ContributorsMontano-Martinez, Karen Vanessa (Author) / Vittal, Vijay (Thesis advisor) / Ayyanar, Raja (Committee member) / Weng, Yang (Committee member) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2022