Mahendra Patel

Impedance-Based Fault Location in Transmission Networks: Theory and Application

A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al.) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.

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 measurement-based 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.

Adaptive wide-area damping control using measurement-driven model considering random time delay and data packet loss

One of the main drawbacks of the existing wide-area damping controller (WADC) that are usually tuned based on several selected typical operating conditions, is its limited adaptability to continuous variations in operating conditions. An adaptive WADC employing the lead-lag structure using measurement-driven model is proposed in this paper. The state subspace model is identified online using ambient data or ring-down data to represent system oscillatory behaviors. The parameters of the lead-lag time constants can be updated based on the new residue derived from the identified model, while the new control gain can also be determined based on the identified model to achieve maximum damping ratio. Moreover, a delay compensator adopting the lead-lag structure and the quadratic interpolation algorithm are utilized to handle random time delay and data packet loss, respectively. The effectiveness of the proposed adaptive WADC is validated by the case study in the two-area four-machine system.

A measurement-based control input-output signal selection approach to damp inter-area oscillations

Wide-area measurement systems enable the wide-area damping controller (WADC) to use remote signals to enhance the small signal stability of large scale interconnected power systems. Due to the global properties, conventional control input-output selection approach based on the detailed mathematical models are not available for the complicated systems. A new measurement-based damping control input- output signal selection approach is proposed based on the residue of a constructed linear autoregressive exogenous (ARX) model, which is applied to derive a low-order black-box transfer function model of a power system with power system stabilizers (PSSs) using wide-area signals. Fast Fourier transform (FFT) analysis is performed to preselect the feedback signals at the dominant mode for ARX model construction. Based on the identified ARX model, the residue is used to select the optimal control input-output pairs. Finally, the selected control input- output signal pair is verified by the control performance comparison in a 16-machine 68-bus power system.

Online Estimation of Steady-State Load Models Considering Data Anomalies

Several techniques have been developed to estimate the load parameters in power systems. Most of the existing algorithms mainly focus on estimating the parameters for offline studies. With on-going smart grid development, high-resolution data at faster rates are available to allow estimation of load parameters in real time. This paper addresses the challenges in online estimation of the load parameters using phasor measurement unit data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, a static load model is used with the Z (constant impedance), I (constant current), and P (constant power) components of the load. Developed estimation algorithms for the ZIP parameter estimation are validated using the IEEE 14-bus system and data provided by the industry collaborators. Simulation results demonstrate the accurate estimation of the ZIP load model using the developed method. Also, various techniques to eliminate anomalies in the input data for accurate estimation of the load parameters have been presented in this paper.

Adaptive and coordinated oscillation damping control using measurement-driven approach

One of the main drawbacks of the existing oscillation damping controllers is that they are designed based on offline simulations for assumed system conditions and are not adaptive to the varying power system operating conditions. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, adaptive oscillation damping controllers can be designed, which can coordinate the control provided by the available actuators and effectively damp targeted oscillation modes. An adaptive and coordinated oscillation damping control using measurement-driven approach is proposed in this paper. The subspace state space model is identified using ambient data or ringdown data to update the parameters of damping controller. Additionally, an adaptive time delay compensator employing a lead-lag structure is utilized to reduce the impact of random time delay. The coordinated control for different oscillation modes is achieved by mode decoupling control through selecting observation signal and actuation signal with minimum interaction with other modes. The demonstration on hardware testbed has illustrated the effectiveness of the proposed adaptive and coordinated damping controller.

PMU-based monitoring of power system stability incorporating load and voltage dynamics

The availability of high-resolution, synchronized measurements from phasor measurement units (PMUs) presents an opportunity to develop advanced analytical techniques for evaluation of power system dynamic performance. This paper proposes a PMU-based technique for online monitoring of power system stability. The algorithm of Maximum Lyapunov Exponent (MLE) is used to determine if a power system swing leads to instability. Advanced models are applied to consider the frequency dynamics of the loads and voltage dynamics of the generators. The effectiveness of the proposed techniques is illustrated on a 179-bus system model. The results show that the MLE technique predicts system stability in real-time. It is also illustrated that, with the extended models, the accuracy for stability prediction is further improved.

Data-driven parameter estimation of steady-state load models

Several techniques have been developed to estimate the load parameters in power systems. Most of the algorithms mainly focus on estimating the parameters for offline studies. With ongoing smart grid development, high-resolution data at fast rates are available to allow load modeling in real-time. This paper addresses the challenges in online estimation of the load parameters using Phasor Measurement Unit (PMU) data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, Z (constant impedance), I (constant current) and P (constant power), ZIP load model is derived. Simulation results for ZIP parameter estimation are presented using the IEEE 14-bus system. Simulation results demonstrate the accurate estimation of the ZIP load model. ZIP parameters are also estimated using the industrial data and the voltage stability limits are computed using actual data to show the impact of ZIP parameters versus the traditional constant power load model.