The advent of synchronized wide-area frequency measurements obtained from frequency disturbance recorders and phasor measurement units has presented the power industry with special opportunities to study power system dynamics. I propose the use of wide-area frequency measurements in identifying system disturbances based on power system post-event modal properties. In this work, power system dynamics are examined from an internal system energy viewpoint. Since an electric power system is composed of coupled rotating machines (large generators) which have air gap magnetic fields that are essentially static, or quasi-static, the power system may be modeled as a system with energy stored in quasi-static magnetic fields. The magnetic fields in the machines do change with time but may be modeled as static as far as wave propagation is concerned. The dynamic model that I develop treats this magnetic energy specifically as potential energy. Each rotating machine also contains an inertia due to the mass and motion of its rotor train and so each machine contains a rotational kinetic energy. Thus the internal system energy for a power system dynamic model may be considered to be contained in potential (magnetic) and kinetic (rotating mass) energies. This notion of internal energy lends itself to the use of a state-space model where each system state is associated with either a kinetic energy or a potential energy. An n-machine system would have a total of 2n states and would thus be a 2n-th order system. For many power system disturbances, I postulate that a linearized version of this model may be used to examine system natural response in terms of frequency and phasor measurements. The disturbances that I will investigate include generator and line outages. For any particular outage, the power system exhibits a very specific natural response in terms of its kinetic and potential energies. Kinetic energy in the system is directly related to each specific machine's rotational speed. I propose that the kinetic energy corresponds directly with bus frequencies through a linear transformation. Likewise magnetic field energy in each machine corresponds directly with a torque angle. The potential energy in the system thus corresponds directly with bus angles through a linear transformation. The primary focus of this work is on frequency deviation modal characteristics – specifically damped oscillation frequencies, mode shapes, and damping ratios. This work presents how specific disturbances on a power system will lead to specific oscillation frequencies in the deviation quantities and that these oscillation frequencies may be used to identify the disturbance. The idea of disturbance identification stems out of previous work done in locating disturbances by using a distributed parameter (DP) model of an electric power system. This DP model, which assumes a wave-like motion of frequency and phase quantities, was used to locate disturbances via a triangulation method. This present work, instead of using a DP model of the power system, assumes lumped parameters and focuses on disturbance identification strictly via modal characteristics – particularly oscillation frequency in the frequency deviations. This model is not concerned with geographic location but focuses on system topology, loading, and machine mass as lumped parameters. Advantages of disturbance identification include mainly reliability enhancements but can also be used in marketing applications. The state-space model used to realize this theory is verified via simulation using small, "academic" systems which should prove useful in classroom settings. Additionally the model is verified on a larger test system in order prove its validity and potential usefulness on large power systems. / Ph. D.

This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems

This thesis proposes, analyses and implements a fast and accurate real-time power system disturbances identification method based on an enhanced linear least squares algorithm for mitigation and monitoring of various power quality problems such as current harmonics, grid unbalances and voltage dips. The enhanced algorithm imposes less real-time computational burden on processing the system and is thus called ???efficient least squares algorithm???. The proposed efficient least squares algorithm does not require matrix inversion operation and contains only real numbers. The number of required real-time matrix multiplications is also reduced in the proposed method by pre-performing some of the matrix multiplications to form a constant matrix. The proposed efficient least squares algorithm extracts instantaneous sine and cosine terms of the fundamental and harmonic components by simply multiplying a set of sampled input data by the pre-calculated constant matrix. A power signal processing system based on the proposed efficient least squares algorithm is presented in this thesis. This power signal processing system derives various power system quantities that are used for real-time monitoring and disturbance mitigation. These power system quantities include constituent components, symmetrical components and various power measurements. The properties of the proposed power signal processing system was studied using modelling and practical implementation in a digital signal processor. These studies demonstrated that the proposed method is capable of extracting time varying power system quantities quickly and accurately. The dynamic response time of the proposed method was less than half that of a fundamental cycle. Moreover, the proposed method showed less sensitivity to noise pollution and small variations in fundamental frequency. The performance of the proposed power signal processing system was compared to that of the popular DFT/FFT methods using computer simulations. The simulation results confirmed the superior performance of the proposed method under both transient and steady-state conditions. In order to investigate the practicability of the method, the proposed power signal processing system was applied to two real-life disturbance mitigation applications namely, an active power filter (APF) and a distribution synchronous static compensator (D-STATCOM). The validity and performance of the proposed signal processing system in both disturbance mitigations applications were investigated by simulation and experimental studies. The extensive modelling and experimental studies confirmed that the proposed signal processing system can be used for practical real-time applications which require fast disturbance identification such as mitigation control and power quality monitoring of power systems