Previously, conventional state estimation techniques have been used for state estimation in power systems. These conventional methods are based on steady state models. As a result of this, power system dynamics during disturbances or transient conditions are not adequately captured. This makes it challenging for operators in control centers to perform visual tracking of the system, proper fault diagnosis and even take adequate preemtive control measures to ensure system stability during voltage dips. Another challenge is that power systems are nonlinear in nature. There are multiple power components in operation at any given time making the system highly dynamic in nature. Consequently, the need to study and implement better dynamic estimation tools that capture system dynamics during disturbances and transient conditions is necessary. For this thesis work, we present the Unscented Kalman Filter (UKF) which integrates Unscented Transformation (UT) to Kalman Filtering. Our algorithm takes as input the output of a synchronous machine modeled in MATLAB/Simulink as well as data from a PMU device assumed to be installed at the terminal bus of the synchronous machine, and estimate the dynamic states of the system using a Kalman Filter. We have presented a detailed and analytical study of our proposed algorithm in estimating two dynamic states of the synchronous machine, rotor angle and rotor speed. Our study and result shows that our proposed methodology has better efficiency when compared to the results of the Extended Kalman Filter (EKF) algorithm in estimating dynamic states of a power system.  Our results are presented and analyzed on the basis of how accurately the algorithm estimates the system states following various simulated transient and small-signal disturbances.

The penetration of renewable energy sources has increased significantly in recent years due to the ongoing depletion of conventional resources and the transition to a low carbon energy system. Renewable energy sources such as wind energy are highly intermittent and unpredictable in nature, which makes the operation of the power grid more dynamic and therefore more complex. In order to operate the power system reliably under such conditions, Phasor Measurement Units (PMUs) through the use of satellite technology can offer a state-of-the-art Wide Area Monitoring System (WAMS) for improving power system monitoring, control and protection. They can improve the operation by providing highly precise and synchronised measurements near to real-time with higher frequency and accuracy. In order to achieve such objectives, a high-speed and reliable communications infrastructure is required to transfer time-critical PMU data from remote locations to the control centre. The signals measured by PMUs are transmitted across Local and Wide Area Networks, where they may encounter excessive delays. Signal delays can have a disruptive effect and make applications at best inefficient and at worse ineffective. The main research contribution of this thesis is the performance evaluation of communication infrastructures for WAMS. The evaluation begins from inside substations and continues over wide areas from substations to control centre. Through laboratory-based investigations and simulations, the performance of communications infrastructure in a typical power system substation has been analysed. In addition, the performance evaluation of WAMS communications infrastructure has been presented. In the modelling and analysis, an existing WAMS as installed on the GB transmission system has been considered. The actual PMU packets as received at the Phasor Data Concentrator (PDC) were captured for latency analysis. A novel algorithmic procedure has been developed and implemented to automate the large-scale latency calculations. Furthermore, the internal delays of PMUs have been investigated, determined and analysed. Subsequently, the WAMS has been simulated and detailed comparisons have been performed between the simulated model results and WAMS performance data captured from the actual WAMS. The validated WAMS model has been used for analysing possible future developments as well as to test newly proposed mechanisms, protocols, etc. in order to improve the communications infrastructure performance.

Big data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.

Fault location reveals the exact information needed for utility crews to timely and promptly perform maintenance and system restoration. Therefore, accurate fault location is a key function in reducing outage time and enhancing power system reliability. Modern power systems are witnessing a trend of integrating more distributed generations (DG) into the grid. DG power outputs may be intermittent and can no longer be treated as constants in fault location method development. DG modeling is also difficult for fault location purpose. Moreover, most existing fault location methods are not applicable to simultaneous faults. To solve the challenges, this dissertation proposes three impedance-based fault location algorithms to pinpoint simultaneous faults for power transmission systems and distribution systems with high penetration of DGs. The proposed fault location algorithms utilize the voltage and/or current phasors that are captured by phasor measurement units. Bus impedance matrix technique is harnessed to establish the relationship between the measurements and unknown simultaneous fault locations. The distinct features of the proposed algorithms are that no fault types and fault resistances are needed to determine the fault locations. In particular, Type I and Type III algorithms do not need the information of source impedances and prefault measurements to locate the faults. Moreover, the effects of shunt capacitance are fully considered to improve fault location accuracy. The proposed algorithms for distribution systems are validated by evaluation studies using Matlab and Simulink SimPowerSystems on a 21 bus distribution system and the modified IEEE 34 node test system. Type II fault location algorithm for transmission systems is applicable to untransposed lines and is validated by simulation studies using EMTP on a 27 bus transmission system. Fault area identification method is proposed to reduce the number of line segments to be examined for fault location. In addition, an optimal fault location method that can identify possible bad measurement is proposed for enhanced fault location estimate. Evaluation studies show that the optimal fault location method is accurate and effective. The proposed algorithms can be integrated into the existing energy management system for enhanced fault management capability for power systems.

The strain on modern electrical power system operation has led to an ever increasing utilization of new Information Communication Technology (ICT) systems to enhance the reliability and efficiency of grid operation. Among these proposals, Phasor Measurement Unit (PMU)-based Wide-Area Monitoring and Control (WAMC) systems have been recognized as one of the enablers of “Smart Grid”, particularly at the transmission level, due to their capability to improve the real-time situational awareness of the grid. These systems differ from the conventional Supervisory Control And Data Acquisition (SCADA) systems in that they provide globally synchronized measurements at high resolutions. On the other hand, the WAMC systems also impose several stringent requirements on the underlying ICT systems, including performance, security, and availability, etc. As a result, the functionality of the WAMC applications is heavily, but not exclusively, dependent on the capabilities of the underlying ICT systems. This tight coupling makes it difficult to fully exploit the benefits of the synchrophasor technology without the proper design and configuration of ICT systems to support the WAMC applications. The strain on modern electrical power system operation has led to an ever increasing utilization of new Information Communication Technology (ICT) systems to enhance the reliability and efficiency of grid operation. Among these proposals, Phasor Measurement Unit (PMU)-based Wide-Area Monitoring and Control (WAMC) systems have been recognized as one of the enablers of “Smart Grid”, particularly at the transmission level, due to their capability to improve the real-time situational awareness of the grid. These systems differ from the conventional Supervisory Control And Data Acquisition (SCADA) systems in that they provide globally synchronized measurements at high resolutions. On the other hand, the WAMC systems also impose several stringent requirements on the underlying ICT systems, including performance, security, and availability, etc. As a result, the functionality of the WAMC applications is heavily, but not exclusively, dependent on the capabilities of the underlying ICT systems. This tight coupling makes it difficult to fully exploit the benefits of the synchrophasor technology without the proper design and configuration of ICT systems to support the WAMC applications. In response to the above challenges, this thesis addresses the dependence of WAMC applications on the underlying ICT systems. Specifically, two of the WAMC system data quality attributes, latency and completeness, are examined together with their effects on a typical WAMC application, PMU-based wide-area damping systems. The outcomes of this research include quantified results in the form of PMU communication delays and data frame losses, and probability distributions that can model the PMU communication delays. Moreover, design requirements are determined for the wide-area damping systems, and three different delay-robust designs for this WAMC application are validated based on the above results. Finally, a virtual PMU is developed to perform power system and communication network co-simulations. The results reported by this thesis offer a prospect for better predictions of the performance of the supporting ICT systems in terms of PMU data latency and completeness. These results can be further used to design and optimize the WAMC applications and their underlying ICT systems in an integrated manner. This thesis also contributes a systematic approach to design the wide-area damping system considering the PMU data latency and completeness. Finally, the developed virtual PMU, as part of a co-simulation platform, provides a means to investigate the dependence of WAMC applications on the capabilities of the underlying ICT systems in a cost-efficient manner. / <p>QC 20131015</p>

Power system stabilizers (PSSs) provide the most economical way to improve damping of electro-mechanical oscillations in electrical power systems. Synchrophasor technology enables the use of remotely measured signals in the PSS allowing for greater flexibility in the design of the PSS. Issues related to the transmission of remote signals should be addressed before implementing such systems in practice. This study investigates two of the data transmission issues: (i) delays, and (ii) data dropout; using a synchrophasor-based PSS designed for a two-area four-generator power system model. A time delayed system is modeled using discrete transformation and the effect of the constant delay on the control action of improving damping of an electro-mechanical oscillation is determined analytically. The effect of random delays and data dropout is investigated using non-linear simulations considering viable remedies to overcome these effects. This research also identifies effective means of using synchrophasor signals for improving the performance of PSSs. Primarily, this research introduces a novel control design algorithm based on eigenstructure assignment that could utilize remotely measured signals to design a robust PSS considering different operating conditions at the design stage. Remote signals could be used as additional inputs to the controller, which introduces extra degrees of freedom. In eigenstructure assignment, these additional degrees of freedom are used to assign eigenvalues and eigenvectors to have adequate damping performance of the system over different operating conditions. The algorithm is formulated as a derivative-free non-linear optimization problem and solved using a single step of optimization by eliminating the use of eigenvalue sensitivities. The proposed algorithm is tested for the 68 bus model of the interconnected New England test system and New York power system. Three different control configurations that use local and remote signals are considered in the design. The algorithm is solved using non-linear simplex optimization considering different initial points for seeking a global solution. Delays in the remote signals are also incorporated into the design. The designed controllers are verified in a non-linear simulation platform. Finally, the reliability of synchrophasor-based PSS is discussed in brief. / February 2016

This thesis develops a robust methodology for on-line identification of power system dynamic signature based on incoming system responses from Phasor Measurement Units (PMUs) in Wide Area Measurement Systems (WAMS). Data mining techniques are used in the methodology to convert real-time monitoring data into transient stability information and the pattern of system dynamic behaviour in the event of instability. The future power system may operate closer to its stability limit in order to improve its efficiency and economic value. The changing types and patterns of load and generation are resulting in highly variable operating conditions. Corrective control and stabilisation is becoming a potentially viable option to enable safer system operation. In the meantime, the number of WAMS projects and PMUs is rising, which will significantly improve the system situational awareness. The combination of all these factors means that it is of vital importance to exploit a new and efficient Transient Stability Assessment (TSA) tool in order to use real-time PMU data to support decisions for corrective control actions. Data mining has been studied as the innovative solution and considered as promising. This work contributes to a number of areas of power systems stability research, specifically around the data driven approach for real-time emergency mode TSA. A review of past research on on-line TSA using PMU measurements and data mining is completed, from which the Decision Tree (DT) method is found to be the most suitable. This method is implemented on the test network. A DT model is trained and the sensitivity of its prediction accuracy is assessed according to a list of network uncertainties. Results showed that DT is a useful tool for on-line TSA for corrective control approach. Following the implementation, a generic probabilistic framework for the assessment of the prediction accuracy of data mining models is developed. This framework is independent of the data mining technique. It performs an exhaustive search of possible contingencies in the testing process and weighs the accuracies according to the realistic probability distribution of uncertain system factors, and provides the system operators with the confidence level of the decisions made under emergency conditions. After that, since the TSA for corrective control usually focuses on transient stability status without dealing with the generator grouping in the event of instability, a two-stage methodology is proposed to address this gap and to identify power system dynamic signature. In this methodology, traditional binary classification is used to identify transient stability in the first stage; Hierarchical Clustering is used to pre-define patterns of unstable dynamic behaviour; and different multiclass classification techniques are investigated to identify the patterns in the second stage. Finally, the effects of practical issues related to WAMS on the data mining methodologies are investigated. Five categories of issues are discussed, including measurement error, communication noise, wide area signal delays, missing measurements, and a limited number of PMUs.

As power system grids continue to grow in order to support an increasing energy demand, the system's behavior accordingly evolves, continuing to challenge designs for maintaining security. It has become apparent in the past few years that, as much as discovering vulnerabilities in the power network, accurate simulations are very critical. This study explores a classification method for validating simulation models, using disturbance measurements from phasor measurement units (PMU). The technique used employs the Random Forest learning algorithm to find a correlation between specific model parameter changes, and the variations in the dynamic response. Also, the measurements used for building and evaluating the classifiers were characterized using Prony decomposition. The generator model, consisting of an exciter, governor, and its standard parameters have been validated using short circuit faults. Single-error classifiers were first tested, where the accuracies of the classifiers built using positive, negative, and zero sequence measurements were compared. The negative sequence measurements have consistently produced the best classifiers, with majority of the parameter classes attaining F-measure accuracies greater than 90%. A multiple-parameter error technique for validation has also been developed and tested on standard generator parameters. Only a few target parameter classes had good accuracies in the presence of multiple parameter errors, but the results were enough to permit a sequential process of validation, where elimination of a highly detectable error can improve the accuracy of suspect errors dependent on the former's removal, and continuing the procedure until all corrections are covered. / Ph. D.

Load models have evolved from simple ZIP model to composite model that incorporates the transient dynamics of motor loads. This research utilizes the latest trend on Machine Learning and builds reliable and accurate composite load model. A composite load model is a combination of static (ZIP) model paralleled with a dynamic model. The dynamic model, recommended by Western Electricity Coordinating Council (WECC), is an induction motor representation. In this research, a dual cage induction motor with 20 parameters pertaining to its dynamic behavior, starting behavior, and per unit calculations is used as a dynamic model. For machine learning algorithms, a large amount of data is required. The required PMU field data and the corresponding system models are considered Critical Energy Infrastructure Information (CEII) and its access is limited. The next best option for the required amount of data is from a simulating environment like PSSE. The IEEE 118 bus system is used as a test setup in PSSE and dynamic simulations generate the required data samples. Each of the samples contains data on Bus Voltage, Bus Current, and Bus Frequency with corresponding induction motor parameters as target variables. It was determined that the Artificial Neural Network (ANN) with multivariate input to single parameter output approach worked best. Recurrent Neural Network (RNN) is also experimented side by side to see if an additional set of information of timestamps would help the model prediction. Moreover, a different definition of a dynamic model with a transfer function-based load is also studied. Here, the dynamic model is defined as a mathematical representation of the relation between bus voltage, bus frequency, and active/reactive power flowing in the bus. With this form of load representation, Long-Short Term Memory (LSTM), a variation of RNN, performed better than the concurrent algorithms like Support Vector Regression (SVR). The result of this study is a load model consisting of parameters defining the load at load bus whose predictions are compared against simulated parameters to examine their validity for use in contingency analysis. / Master of Science / Independent system Operators (ISO) and Distribution system operators (DSO) have a responsibility to provide uninterrupted power supply to consumers. That along with the longing to keep operating cost minimum, engineers and planners study the system beforehand and seek to find the optimum capacity for each of the power system elements like generators, transformers, transmission lines, etc. Then they test the overall system using power system models, which are mathematical representation of the real components, to verify the stability and strength of the system. However, the verification is only as good as the system models that are used. As most of the power systems components are controlled by the operators themselves, it is easy to develop a model from their perspective. The load is the only component controlled by consumers. Hence, the necessity of better load models. Several studies have been made on static load modeling and the performance is on par with real behavior. But dynamic loading, which is a load behavior dependent on time, is rather difficult to model. Some attempts on dynamic load modeling can be found already. Physical component-based and mathematical transfer function based dynamic models are quite widely used for the study. These load structures are largely accepted as a good representation of the systems dynamic behavior. With a load structure in hand, the next task is estimating their parameters. In this research, we tested out some new machine learning methods to accurately estimate the parameters. Thousands of simulated data are used to train machine learning models. After training, we validated the models on some other unseen data. This study finally goes on to recommend better methods to load modeling.

The world of power systems is ever changing; ever evolving. One such evolution was the advent of Phasor Measurement Units (PMUs). With the introduction of PMUs in the field, power system monitoring and control changed for the better. Innovative and efficient algorithms that used synchrophasors came to be written. To make these algorithms robust, it became necessary to remove errors that crept into the power system with time and usage. Thus the process of calibration became essential when practical decisions started being made based on PMU measurements. In the context of this thesis ‘calibration’ is the method used to estimate a correction factor which, when multiplied with the respective measurement, negates the effect of any errors that might have crept into them due to the instrument transformers located at the inputs of a PMU or the PMU device itself. Though this thesis mainly deals with the calibration of instrument transformers, work has been done previously for calibrating other components of a power system. A brief description of those methods have been provided along with a history on instrument transformer calibration. Three new methodologies for instrument transformer calibration have been discussed in details in this thesis. The first method describes how only voltage transformers can be calibrated by placing optimal number of good quality voltage measurements at strategic locations in the grid, in presence of ratio errors in the instrument transformers and Gaussian errors in the PMUs. The second method provides a way to calibrate all instrument transformers (both current and voltage) in presence of only one good quality voltage measurement located at the end of a tie-line. This method assumes that all the instrument transformers have ratio errors and the PMUs have quantization errors. The third method attains the same objective as the second one, with the additional constraint that the data obtained from the field may be contaminated. Thus, the third method shows how calibration of all the instrument transformers can be done with data that is intermittent and is therefore, the most practical approach (of the three) for instrument transformer calibration. / Master of Science