Matching Items (14)

132048-Thumbnail Image.png

The Enhancement of Power System Stability Under Different Fault Conditions (East Java-Bali 2018 Blackout)

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

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.

Contributors

Agent

Created

Date Created
  • 2019-12

156921-Thumbnail Image.png

Transmission Line Parameter Estimation using Synchrophasor Data

Description

Transmission line parameters play an important role in state estimation, dynamic line rating, and fault analysis. Because of this, several methods have been proposed in the literature for line parameter

Transmission line parameters play an important role in state estimation, dynamic line rating, and fault analysis. Because of this, several methods have been proposed in the literature for line parameter estimation, especially using synchrophasor data. However, success of most prior research has been demonstrated using purely synthetic data. A synthetic dataset does not have the problems encountered with real data, such as invariance of measurements and realistic field noise. Therefore, the algorithms developed using synthetic datasets may not be as effective when used in practice. On the other hand, the true values of the line parameters are unknown and therefore the algorithms cannot be directly implemented on real data. A multi-stage test procedure is developed in this work to circumvent this problem.

In this thesis, two popular algorithms, namely, moving-window total least squares (MWTLS) and recursive Kalman filter (RKF) are applied on real data in multiple stages. In the first stage, the algorithms are tested on a purely synthetic dataset. This is followed by testing done on pseudo-synthetic datasets generated using real PMU data. In the final stage, the algorithms are implemented on the real PMU data obtained from a local utility. The results show that in the context of the given problem, RKF has better performance than MWTLS. Furthermore, to improve the performance of RKF on real data, ASPEN data are used to calculate the initial estimates. The estimation results show that the RKF algorithm can reliably estimate the sequence impedances, using ASPEN data as a starting condition. The estimation procedure is repeated over different time periods and the corresponding results are presented.

Finally, the significance of data drop-outs and its impact on the use of parameter estimates for real-time power system applications, such as state estimation and dynamic line rating, is discussed. To address the problem (of data drop-outs), an auto regressive integrated moving average (ARIMA) model is implemented. The ability of this model to predict the variations in sequence impedances is demonstrated.

Contributors

Agent

Created

Date Created
  • 2018

158069-Thumbnail Image.png

Stacked-Value of Battery Storage: Effect of Battery Storage Penetration on Power Dispatch

Description

In this work, the stacked values of battery energy storage systems (BESSs) of various power and energy capacities are evaluated as they provide multiple services such as peak shaving, frequency

In this work, the stacked values of battery energy storage systems (BESSs) of various power and energy capacities are evaluated as they provide multiple services such as peak shaving, frequency regulation, and reserve support in an ‘Arizona-based test system’ - a simplified, representative model of Salt River Project’s (SRP) system developed using the resource stack information shared by SRP. This has been achieved by developing a mixed-integer linear programming (MILP) based optimization model that captures the operation of BESS in the Arizona-based test system. The model formulation does not include any BESS cost as the objective is to estimate the net savings in total system operation cost after a BESS is deployed in the system. The optimization model has been formulated in such a way that the savings due to the provision of a single service, either peak shaving or frequency regulation or spinning reserve support, by the BESS, can be determined independently. The model also allows calculation of combined savings due to all the services rendered by the BESS.

The results of this research suggest that the savings obtained with a BESS providing multiple services are significantly higher than the same capacity BESS delivering a single service in isolation. It is also observed that the marginal contribution of BESS reduces with increasing BESS energy capacity, a result consistent with the law of diminishing returns. Further, small changes in the simulation environment, such as factoring in generator forced outage rates or projection of future solar penetration, can lead to changes as high as 10% in the calculated stacked value.

Contributors

Agent

Created

Date Created
  • 2020

Evaluation of High Temperature Operation of Natural Ester Filled Distribution Transformers: A Techno-economic Analysis

Description

The lifetime of a transformer is essentially determined by the life of its insulation

system which is a time function of the temperature defined by its thermal class. A large

quantity of

The lifetime of a transformer is essentially determined by the life of its insulation

system which is a time function of the temperature defined by its thermal class. A large

quantity of studies and international standards have been published indicating the

possibility of increasing the thermal class of cellulose based materials when immersed

in natural esters which are superior to traditional mineral oils. Thus, a transformer

having thermally upgraded Kraft paper and natural ester dielectric fluid can be

classified as a high temperature insulation system. Such a transformer can also

operate at temperatures 20C higher than its mineral oil equivalent, holding additional

loading capability without losing life expectancy. This thesis focuses on evaluating

the use of this feature as an additional capability for enhancing the loadability and/or

extending the life of the distribution transformers for the Phoenix based utility - SRP

using FR3 brand natural ester dielectric fluid.

Initially, different transformer design options to use this additional loadability

are compared allowing utilities to select an optimal FR3 filled transformer design

for their application. Yearlong load profiles for SRP distribution transformers, sized

conventionally on peak load demands, are analyzed for their oil temperatures, winding

temperatures and loss of insulation life. It is observed that these load profiles can be

classified into two types: 1) Type-1 profiles with high peak and high average loads,

and 2) Type-2 profiles with comparatively low peak and low average load.

For the Type 1 load profiles, use of FR3 natural ester fluid with the same nominal

rating showed 7.4 times longer life expectation. For the Type 2 load profiles, a new

way of sizing ester filled transformers based on both average and peak load, instead of

only peak load, called “Sustainable Peak Loading” showed smaller size transformers

can handle the same yearly peak loads while maintaining superior insulation lifespan.

It is additionally possible to have reduction in the total energy dissipation over the

year. A net present value cost savings up to US$1200 per transformer quantifying

benefits of the life extension and the total ownership cost savings up to 30% for

sustainable peak loading showed SRP distribution transformers can gain substantial

economic savings when the distribution transformer fleet is replaced with FR3 ester

filled units.

Contributors

Agent

Created

Date Created
  • 2018

158193-Thumbnail Image.png

Methodology for Identifying Inverter-based Renewable Generation Penetration Threshold in a Power System

Description

Energy is one of the wheels on which the modern world runs. Therefore, standards and limits have been devised to maintain the stability and reliability of the power grid. This

Energy is one of the wheels on which the modern world runs. Therefore, standards and limits have been devised to maintain the stability and reliability of the power grid. This research shows a simple methodology for increasing the amount of Inverter-based Renewable Generation (IRG), which is also known as Inverter-based Resources (IBR), for that considers the voltage and frequency limits specified by the Western Electricity Coordinating Council (WECC) Transmission Planning (TPL) criteria, and the tie line power flow limits between the area-under-study and its neighbors under contingency conditions. A WECC power flow and dynamic file is analyzed and modified in this research to demonstrate the performance of the methodology. GE's Positive Sequence Load Flow (PSLF) software is used to conduct this research and Python was used to analyze the output data.

The thesis explains in detail how the system with 11% of IRG operated before conducting any adjustments (addition of IRG) and what procedures were modified to make the system run correctly. The adjustments made to the dynamic models are also explained in depth to give a clearer picture of how each adjustment affects the system performance. A list of proposed IRG units along with their locations were provided by SRP, a power utility in Arizona, which were to be integrated into the power flow and dynamic files. In the process of finding the maximum IRG penetration threshold, three sensitivities were also considered, namely, momentary cessation due to low voltages, transmission vs. distribution connected solar generation, and stalling of induction motors. Finally, the thesis discusses how the system reacts to the aforementioned modifications, and how IRG penetration threshold gets adjusted with regards to the different sensitivities applied to the system.

Contributors

Agent

Created

Date Created
  • 2020

155730-Thumbnail Image.png

Exploration of a Scalable Holomorphic Embedding Method Formulation for Power System Analysis Applications

Description

The holomorphic embedding method (HEM) applied to the power-flow problem (HEPF) has been used in the past to obtain the voltages and flows for power systems. The incentives for using

The holomorphic embedding method (HEM) applied to the power-flow problem (HEPF) has been used in the past to obtain the voltages and flows for power systems. The incentives for using this method over the traditional Newton-Raphson based nu-merical methods lie in the claim that the method is theoretically guaranteed to converge to the operable solution, if one exists.

In this report, HEPF will be used for two power system analysis purposes:

a. Estimating the saddle-node bifurcation point (SNBP) of a system

b. Developing reduced-order network equivalents for distribution systems.

Typically, the continuation power flow (CPF) is used to estimate the SNBP of a system, which involves solving multiple power-flow problems. One of the advantages of HEPF is that the solution is obtained as an analytical expression of the embedding parameter, and using this property, three of the proposed HEPF-based methods can es-timate the SNBP of a given power system without solving multiple power-flow prob-lems (if generator VAr limits are ignored). If VAr limits are considered, the mathemat-ical representation of the power-flow problem changes and thus an iterative process would have to be performed in order to estimate the SNBP of the system. This would typically still require fewer power-flow problems to be solved than CPF in order to estimate the SNBP.

Another proposed application is to develop reduced order network equivalents for radial distribution networks that retain the nonlinearities of the eliminated portion of the network and hence remain more accurate than traditional Ward-type reductions (which linearize about the given operating point) when the operating condition changes.

Different ways of accelerating the convergence of the power series obtained as a part of HEPF, are explored and it is shown that the eta method is the most efficient of all methods tested.

The local-measurement-based methods of estimating the SNBP are studied. Non-linear Thévenin-like networks as well as multi-bus networks are built using model data to estimate the SNBP and it is shown that the structure of these networks can be made arbitrary by appropriately modifying the nonlinear current injections, which can sim-plify the process of building such networks from measurements.

Contributors

Agent

Created

Date Created
  • 2017

158293-Thumbnail Image.png

Unobservable False Data Injection Attacks on Power Systems

Description

Reliable operation of modern power systems is ensured by an intelligent cyber layer that monitors and controls the physical system. The data collection and transmission is achieved by the supervisory

Reliable operation of modern power systems is ensured by an intelligent cyber layer that monitors and controls the physical system. The data collection and transmission is achieved by the supervisory control and data acquisition (SCADA) system, and data processing is performed by the energy management system (EMS). In the recent decades, the development of phasor measurement units (PMUs) enables wide area real-time monitoring and control. However, both SCADA-based and PMU-based cyber layers are prone to cyber attacks that can impact system operation and lead to severe physical consequences.

This dissertation studies false data injection (FDI) attacks that are unobservable to bad data detectors (BDD). Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical power flow on a target line. However, the results were only demonstrated on small systems assuming that they are operated with DC optimal power flow (OPF). This dissertation is divided into four parts to thoroughly understand the consequences of these attacks as well as develop countermeasures.

The first part focuses on evaluating the vulnerability of large-scale power systems to FDI attacks. The solution technique introduced in prior work to solve the ADBLP is intractable on large-scale systems due to the large number of binary variables. Four new computationally efficient algorithms are presented to solve this problem.

The second part studies vulnerability of N-1 reliable power systems operated by state-of-the-art EMSs commonly used in practice, specifically real-time contingency analysis (RTCA), and security-constrained economic dispatch (SCED). An ADBLP is formulated with detailed assumptions on attacker's knowledge and system operations.

The third part considers FDI attacks on PMU measurements that have strong temporal correlations due to high data rate. It is shown that predictive filters can detect suddenly injected attacks, but not gradually ramping attacks.

The last part proposes a machine learning-based attack detection framework consists of a support vector regression (SVR) load predictor that predicts loads by exploiting both spatial and temporal correlations, and a subsequent support vector machine (SVM) attack detector to determine the existence of attacks.

Contributors

Agent

Created

Date Created
  • 2020

157471-Thumbnail Image.png

Machine Learning Applications for Dynamic Security Assessment in presence of Renewable Generation and Load Induced Variability

Description

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).

This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.

Contributors

Agent

Created

Date Created
  • 2019

156196-Thumbnail Image.png

Enhanced Reserve Procurement Policies for Power Systems with Increasing Penetration Levels of Stochastic Resources

Description

The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages)

The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost.

Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements.

This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.

Contributors

Agent

Created

Date Created
  • 2018

158867-Thumbnail Image.png

A Machine Learning based High-Speed State Estimator for Partially Observed Electric Transmission Systems

Description

The accurate monitoring of the bulk transmission system of the electric power grid by sensors, such as Remote Terminal Units (RTUs) and Phasor Measurement Units (PMUs), is essential for maintaining

The accurate monitoring of the bulk transmission system of the electric power grid by sensors, such as Remote Terminal Units (RTUs) and Phasor Measurement Units (PMUs), is essential for maintaining the reliability of the modern power system. One of the primary objectives of power system monitoring is the identification of the snapshots of the system at regular intervals by performing state estimation using the available measurements from the sensors. The process of state estimation corresponds to the estimation of the complex voltages at all buses of the system. PMU measurements play an important role in this regard, because of the time-synchronized nature of these measurements as well as the faster rates at which they are produced. However, a model-based linear state estimator created using PMU-only data requires complete observability of the system by PMUs for its continuous functioning. The conventional model-based techniques also make certain assumptions in the modeling of the physical system, such as the constant values of the line parameters. The measurement error models in the conventional state estimators are also assumed to follow a Gaussian distribution. In this research, a data mining technique using Deep Neural Networks (DNNs) is proposed for performing a high-speed, time-synchronized state estimation of the transmission system of the power system. The proposed technique uses historical data to identify the correlation between the measurements and the system states as opposed to directly using the physical model of the system. Therefore, the highlight of the proposed technique is its ability to provide an accurate, fast, time-synchronized estimate of the system states even in the absence of complete system observability by PMUs.
The state estimator is formulated for the IEEE 118-bus system and its reliable performance is demonstrated in the presence of redundant observability, complete observability, and incomplete observability. The robustness of the state estimator is also demonstrated by performing the estimation in presence of Non-Gaussian measurement errors and varying line parameters. The consistency of the DNN state estimator is demonstrated by performing state estimation for an entire day.

Contributors

Agent

Created

Date Created
  • 2020