In power system studies, some parameters cannot be measured directly by using the currently existing power meters. These power system parameters include the power system Thevenin impedance, harmonic impedances, zero sequence impedance, and the load model parameters. The power systems Thevenin impedance at a load bus is an important parameter for power system planning and operation. The effects of changing the system operation conditions on voltages at the load buses can be easily determined if the power system impedance parameters are known. Moreover, by knowing the Thevenin equivalent parameters, one can calculate the systems voltage stability margin and maximum loadability. The knowledge of power system harmonic impedances is necessary for harmonic mitigation, determination of harmonic limit compliance, prediction of system resonance, and harmonic propagation studies. Moreover, real-time monitoring of the systems harmonic impedances provides significant improvements to the design and operation of active filters. The systems zero sequence impedance at the substation bus is also important information for power system studies. It is used to calculate the different ground fault levels at substations. Furthermore, the loads in power systems play a significant role in power system planning, control, and stability analysis. Having reliable and accurate models of the loads is essential for designing automatic control systems and optimizing their configuration. Obtaining such models has been a challenging problem for power system engineers for decades, especially in the current deregulated market environment. This thesis presents newly developed and verified algorithms for online measurement and monitoring of these power system parameters. The algorithm proposed for monitoring the systems Thevenin, harmonic, and zero sequence impedance parameters, uses the natural variations of the loads connected to the substations. The proposed algorithm for monitoring of load model parameters uses the voltage and current waveforms captured during the operation of the Under Load Tap Change (ULTC) transformers installed in the distribution substations. The proposed algorithms are applied to several field measurements from different substations. The results show that the algorithms fulfill the requirements for the online measurement and monitoring of power system Thevenin, harmonic and zero sequence impedances as well as the load model parameters. / Energy Systems

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As the requirements to operate the electric power system become more stringent and operating costs must be kept to a minimum, operators and planners must ensure that power system models are accurate and capable of replicating system disturbances. Traditionally, load models were represented as static ZIP models; however, NERC has recently required that planners model the transient dynamics of motor loads to study their effect on the postdisturbance behavior of the power system. Primarily, these studies are to analyze the effects of fault-induced, delayed voltage recovery, which could lead to cascading voltage stability issues. Genetic algorithms and constrained multivariable function minimization are global and local optimization tools used to extract static and dynamic load model parameters from postdisturbance data. The genetic algorithm's fitness function minimizes the difference between measured and calculated real and reactive power by varying the model parameters. The fitness function of the genetic algorithm, a function of voltage and frequency, evaluates an individual\'s difference between measured and simulated real and reactive power. While real measured data was unavailable, simulations in PSS/E were used to create data, and then compared against estimated data to examine the algorithms' ability to estimate parameters. / Master of Science

Power systems must face new challenges in the current environment. The energy market liberalization and the increase in the loading level make the occurrence of instability phenomena leading to large blackouts more likely. Existing tools must be improved and new tools must be developed to avoid them. The aim of this thesis is the improvement of the voltage stability assessment efficiency. Two orientations are studied: the monitoring methods and the load modeling. The purpose of the monitoring methods is to evaluate the voltage stability using only measurements and without running simulations. The first approach considered is local. The parameters of the Thevenin equivalent seen from a load bus are assessed thanks to a stream of local voltage and current measurements. Several issues are investigated using measurements coming from complete time-domain simulations. The applicability of this approach is questioned. The second approach is global and uses measurements acquired by a Wide-Area Measurement System (WAMS). An original approach with a certain prediction capability is proposed, along with intuitive visualizations that allow to understand the deterioration process leading to the collapse. The load modeling quality is certainly the weak point of the voltage security assessment tools which run simulations to predict the stability of the power system depending on different evolutions. Appropriate load models with accurate parameters lead to a direct improvement of the prediction precision. An innovative procedure starting from data of long measurement campaigns is proposed to automatically evaluate the parameters of static and dynamic load models. Real measurements taken in the Belgian power system are used to validate this approach.

abstract: This dissertation presents innovative techniques to develop performance-based models and complete transient models of induction motor drive systems with vector controls in electro-magnetic transient (EMT) and positive sequence transient stability (PSTS) simulation programs. The performance-based model is implemented by obtaining the characteristic transfer functions of perturbed active and reactive power consumptions with respect to frequency and voltage perturbations. This level of linearized performance-based model is suitable for the investigation of the damping of small-magnitude low-frequency oscillations. The complete transient model is proposed by decomposing the motor, converter and control models into d-q axes components and developing a compatible electrical interface to the positive-sequence network in the PSTS simulators. The complete transient drive model is primarily used to examine the system response subject to transient voltage depression considering increasing penetration of converter-driven motor loads. For developing the performance-based model, modulations are performed on the supply side of the full drive system to procure magnitude and phase responses of active and reactive powers with respect to the supply voltage and frequency for a range of discrete frequency points. The prediction error minimization (PEM) technique is utilized to generate the curve-fitted transfer functions and corresponding bode plots. For developing the complete drive model in the PSTS simulation program, a positive-sequence voltage source is defined properly as the interface of the model to the external system. The dc-link of the drive converter is implemented by employing the average model of the PWM converter, and is utilized to integrate the line-side rectifier and machine-side inverter. Numerical simulation is then conducted on sample test systems, synthesized with suitable characteristics to examine performance of the developed models. The simulation results reveal that with growing amount of drive loads being distributed in the system, the small-signal stability of the system is improved in terms of the desirable damping effects on the low-frequency system oscillations of voltage and frequency. The transient stability of the system is also enhanced with regard to the stable active power and reactive power controls of the loads, and the appropriate VAr support capability provided by the drive loads during a contingency. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

In smart energy systems, load curve refers to the time series reported by smart meters, which indicate the energy consumption of customers over a certain period of time. The widespread use of load curve (data) in demand side management and demand response programs makes it one of the most important resources. To capture the load behavior or energy consumption patterns, load curve modeling is widely applied to help the utilities and residents make better plans and decisions. In this dissertation, with the help of load curve modeling, we focus on data-driven solutions to three load monitoring problems in different scenarios of smart energy systems, including residential power systems and datacenter power systems and covering the research fields of i) data cleansing, ii) energy disaggregation, and iii) fine-grained power monitoring. First, to improve the data quality for load curve modeling on the supply side, we challenge the regression-based approaches as an efficient way to load curve data cleansing and propose a new approach to analyzing and organizing load curve data. Our approach adopts a new view, termed portrait, on the load curve data by analyzing the inherent periodic patterns and re-organizing the data for ease of analysis. Furthermore, we introduce strategies to build virtual portrait datasets and demonstrate how this technique can be used for outlier detection in load curve. To identify the corrupted load curve data, we propose an appliance-driven approach that particularly takes advantage of information available on the demand side. It identifies corrupted data from the smart meter readings by solving a carefully-designed optimization problem. To solve the problem efficiently, we further develop a sequential local optimization algorithm that tackles the original NP-hard problem by solving an approximate problem in polynomial time. Second, to separate the aggregated energy consumption of a residential house into that of individual appliances, we propose a practical and universal energy disaggregation solution, only referring to the readily available information of appliances. Based on the sparsity of appliances' switching events, we first build a sparse switching event recovering (SSER) model. Then, by making use of the active epochs of switching events, we develop an efficient parallel local optimization algorithm to solve our model and obtain individual appliances' energy consumption. To explore the benefit of introducing low-cost energy meters for energy disaggregation, we propose a semi-intrusive appliance load monitoring (SIALM) approach for large-scale appliances situation. Instead of using only one meter, multiple meters are distributed in the power network to collect the aggregated load data from sub-groups of appliances. The proposed SSER model and parallel optimization algorithm are used for energy disaggregation within each sub-group of appliances. We further provide the sufficient conditions for unambiguous state recovery of multiple appliances, under which a minimum number of meters is obtained via a greedy clique-covering algorithm. Third, to achieve fine-grained power monitoring at server level in legacy datacenters, we present a zero-cost, purely software-based solution. With our solution, no power monitoring hardware is needed any more, leading to much reduced operating cost and hardware complexity. In detail, we establish power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. We implement and evaluate our solution over a real-world datacenter with 326 servers. The results show that our solution can provide high precision power estimation at both the rack level and the server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at the rack level has mean relative error of 2.18%, and ii) at the server level has mean relative errors of 9.61% and 7.53% corresponding to the idle and peak power, respectively. / Graduate / 0984 / 0791 / 0800 / tangguo1999@gmail.com

O objetivo principal deste trabalho é o desenvolvimento de algoritmos para análise de estabilidade transitória com esforço computacional reduzido. Com este propósito é desenvolvido uma metodologia para o equivalente dinâmico, a qual é um resultado da eliminação das barras de cargas providas com diferentes modelagens: PQ como constante, PQ como função de tensão e PQ como função de tensão e frequência. O sistema elétrico de potência é reduzido à barras geradoras internas através do método clássico de determinação de equivalentes desenvolvidos por Ward. Uma versão melhorada do equivalente de Ward atualiza a injeção de corrente equivalente nas barras geradoras internas através da utilização das Unidades Medidoras de fasores (PMU\'s) que fornecem os desvios angulares de potência das unidades geradoras e ele uma matriz ele sensibilidade W. A atualização da injeção ele corrente equivalente nas barras geradoras internas é efetuada através da matriz de sensibilidade W para cada novo ponto ele operação. A técnica utilizada para o desenvolvimento da matriz de sensibilidade W foi feita atráves da linearização da equação do fluxo de carga não linear e considerando diferentes modelagens das cargas. Programas que levam em consideração os aspectos acima foram desenvolvidos onde as diferentes modelagens de cargas são incorporadas. Para isto, o sistema AEP de 14 barras foi utilizado como teste para este projeto. / The development of algorithms for analysis of transient stability with reduced computational effort is the main objective of this work. With that purpose a methodology to build reduced equivalents of a power system in a dynamic way is developed. The expression for the injected equivalent currents at the generator buses, is a result of load buses elimination corresponding to different load models, that is: constant PQ loads, PQ as voltage dependency model,as well as PQ as voltage and frequency dependency models. The electric power system is reduced to the generator buses only, using the classic methodology for equivalents developed by Ward. An improved version for the Ward equivalent updates the injected equivalent currents at the generator buses through the use of the phasor measurement units (PMU\'s) and a sensitivity matrix W; those units give the power angle deviation of the generators when some perturbation is applied to the system. In the process of correcting the injected equivalent currents the sensitivity matrix W is updated every time the operating point changes significatively. The technique used in developing the sensitivity matrix W was made through linearization of the load flow equations applied to the different load models. Computer programs taking the considerations above mentioned were developed. The AEP-14 bus power system was used to test the models proposed on this work.

abstract: Voltage stability is always a major concern in power system operation. Recently Fault Induced Delayed Voltage Recovery (FIDVR) has gained increased attention. It is widely believed that the motor-driven loads of high efficiency, low inertia air conditioners are one of the main causes of FIDVR events. Simulation tools that assist power system operation and planning have been found insufficient to reproduce FIDVR events. This is because of their inaccurate load modeling of single-phase motor loads. Conventionally three-phase motor models have been used to represent the aggregation effect of single-phase motor load. However researchers have found that this modeling method is far from an accurate representation of single-phase induction motors. In this work a simulation method is proposed to study the precise influence of single-phase motor load in context of FIDVR. The load, as seen the transmission bus, is replaced with a detailed distribution system. Each single-phase motor in the distribution system is represented by an equipment-level model for best accuracy. This is to enable the simulation to capture stalling effects of air conditioner compressor motors as they are related to FIDVR events. The single phase motor models are compared against the traditional three phase aggregate approximation. Also different percentages of single-phase motor load are compared and analyzed. Simulation result shows that proposed method is able to reproduce FIDVR events. This method also provides a reasonable estimation of the power system voltage stability under the contingencies. / Dissertation/Thesis / M.S. Electrical Engineering 2012

O objetivo principal deste trabalho é o desenvolvimento de algoritmos para análise de estabilidade transitória com esforço computacional reduzido. Com este propósito é desenvolvido uma metodologia para o equivalente dinâmico, a qual é um resultado da eliminação das barras de cargas providas com diferentes modelagens: PQ como constante, PQ como função de tensão e PQ como função de tensão e frequência. O sistema elétrico de potência é reduzido à barras geradoras internas através do método clássico de determinação de equivalentes desenvolvidos por Ward. Uma versão melhorada do equivalente de Ward atualiza a injeção de corrente equivalente nas barras geradoras internas através da utilização das Unidades Medidoras de fasores (PMU\'s) que fornecem os desvios angulares de potência das unidades geradoras e ele uma matriz ele sensibilidade W. A atualização da injeção ele corrente equivalente nas barras geradoras internas é efetuada através da matriz de sensibilidade W para cada novo ponto ele operação. A técnica utilizada para o desenvolvimento da matriz de sensibilidade W foi feita atráves da linearização da equação do fluxo de carga não linear e considerando diferentes modelagens das cargas. Programas que levam em consideração os aspectos acima foram desenvolvidos onde as diferentes modelagens de cargas são incorporadas. Para isto, o sistema AEP de 14 barras foi utilizado como teste para este projeto. / The development of algorithms for analysis of transient stability with reduced computational effort is the main objective of this work. With that purpose a methodology to build reduced equivalents of a power system in a dynamic way is developed. The expression for the injected equivalent currents at the generator buses, is a result of load buses elimination corresponding to different load models, that is: constant PQ loads, PQ as voltage dependency model,as well as PQ as voltage and frequency dependency models. The electric power system is reduced to the generator buses only, using the classic methodology for equivalents developed by Ward. An improved version for the Ward equivalent updates the injected equivalent currents at the generator buses through the use of the phasor measurement units (PMU\'s) and a sensitivity matrix W; those units give the power angle deviation of the generators when some perturbation is applied to the system. In the process of correcting the injected equivalent currents the sensitivity matrix W is updated every time the operating point changes significatively. The technique used in developing the sensitivity matrix W was made through linearization of the load flow equations applied to the different load models. Computer programs taking the considerations above mentioned were developed. The AEP-14 bus power system was used to test the models proposed on this work.

In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately. / Ph. D.