ARMAX-based transfer function model identification using wide-area measurement for adaptive and coordinated damping control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. This paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. The results demonstrate that the measurementbased model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control

Wide-Area Measurement-Driven Approaches for Power System Modeling and Analytics

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising. Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses. Furthermore, since auto-regressive models are in a big family, the ARX model can be modified as other models for various purposes. A multi-input multi-output (MIMO) auto-regressive moving average with exogenous inputs (ARMAX) model is introduced to identify a low-order transfer function model of power systems for adaptive and coordinated damping control. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify an online measurement-based transfer function model that can be used to tune the oscillation damping controller. A demonstration on hardware testbed may illustrate the effectiveness of the proposed adaptive and coordinated damping controller. In fact, measurement-driven approaches for power system modeling and analytics are also attractive to the power industry since a huge number of monitoring devices are deployed in substations and power plants. However, most current systems for collecting and monitoring data are isolated, thereby obstructing the integration of the various data into a holistic model. To improve the capability of utilizing big data and leverage wide-area measurement-driven approaches in the power industry, this dissertation also describes a comprehensive solution through building out an enterprise-level data platform based on the PI system to support data-driven applications and analytics. One of the applications is to identify transmission-line parameters using PMU data. The identification can obtain more accurate parameters than the current parameters in PSS®E and EMS after verifying the calculation results in EMS state estimation. In addition, based on temperature information from online asset monitoring, the impact of temperature change can be observed by the variance of transmission-line resistance

A three-level distributed architecture for the real-time monitoring of modern power systems

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model

Dynamic Equivalent Modeling and Stability Analysis of Electric Power Systems

Interconnected power systems are increasing in both size and complexity. For such large-scale power systems, very accurate full-order dynamic system models are computational intensive to perform dynamic studies. In this paper, a measurement-based dynamic equivalent method is proposed to derive reduced models of large power systems. Specifically, a set of measurements at the boundary nodes between the study area and the external area are employed in model parameter identification. The proposed method is validated using simulation results obtained from both 140-bus NPCC system and 9,000-machine 70,000-bus U.S. Eastern Interconnection (EI) system. The results demonstrate that the measurement-based equivalent technique can capture the external system behaviors precisely. Compared with traditional generator equivalencing method, the proposed measurement-based model has higher accuracy but lower order and improved computational efficiency.The intermittence and fluctuation of renewable generations bring unprecedentedly challenges to the power system reliability and resilience. To keep the lights on after contingencies such as the loss of two largest generation units, it is imperative for a power system to have sufficient frequency response reserve (FRR) to ensure that the decay in system frequency would be arrested before triggering under frequency load shedding (UFLS) schemes. In this paper, a method to derive the EI minimum FRR requirement in real-time will be developed. This minimum FRR will help the EI operators decrease the current FRR requirement and accommodate more renewable generations while achieving a saving of both energy and facility costs. Most importantly, the ability to adaptively vary the FRR will provide the additional agility, resiliency, and reliability to the grid

Structural system identification: Structural dynamics model validation

Structural system identification is concerned with the development of systematic procedures and tools for developing predictive analytical models based on a physical structure`s dynamic response characteristics. It is a multidisciplinary process that involves the ability (1) to define high fidelity physics-based analysis models, (2) to acquire accurate test-derived information for physical specimens using diagnostic experiments, (3) to validate the numerical simulation model by reconciling differences that inevitably exist between the analysis model and the experimental data, and (4) to quantify uncertainties in the final system models and subsequent numerical simulations. The goal of this project was to develop structural system identification techniques and software suitable for both research and production applications in code and model validation

Small signal stability analysis of a four-machine system with placement of multi-terminal high voltage direct current link

Inter-area oscillation caused by weak interconnected lines or low generator inertia is a critical problem facing power systems. This study investigated the performance analysis of a multi-terminal high voltage direct current (MTDC) on the damping of inter-area oscillations of a modified two-area four-machine network. Two case studies were considered, utilising scenario 1: a double alternating current (AC) circuit in linking Bus_10 and Bus_11; and scenario 2: a three-terminal line commutated converter high voltage direct current system in linking Bus_6 and Bus_11 into Bus_9. It was found that scenario 2 utilising MTDC link with a robust controller provided quick support in minimising the network oscillations following a fault on the system. The MTDC converter controllers’ setup offered sufficient support for the inertia of the AC system, thus providing efficient damping of the inter-area oscillation of the system