(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Originally, ancillary services (ASs) were provided by large-scale power plants to ensure the secure and reliable operation of power systems. However, with the advent of renewable energy sources either in large-scale or as smaller distributed generation units, there is a strong need to revisit the structure and aim of traditional ASs and reconsider them under two main concepts; ASs provided to distribution networks (DNs), aiming to solve local issues at the distribution level, and ASs offered to transmission system (TS), targeting to solve issues having a wider impact on the power system. The former can be considered as DN-oriented ASs, while the latter as TS-oriented ASs. In this changing environment, the increasing installation of measuring infrastructure has created new sources of data, providing the opportunity to improve the performance of several ASs by applying measurement-based analysis. This review paper gives an insight into the most important DN- and TS-oriented ASs foreseen to apply in future power systems. Additionally, state-of-the-art on measurement-based analysis techniques that can be used to facilitate the provision of several ASs by providing in real-time critical information regarding system dynamic performance is presented.

Ancillary services in active distribution networks: A review of technological trends from operational and online analysis perspective

Multi-dimensional ringdown modal analysis by filtering

Modal analysis of active distribution networks using system identification techniques

Identifying coherent generating units in power systems is an important step toward development of a reduced model for bulk electricity grid aiming at wide-area analysis and controlled islanding practices. In this paper, an approach for identification of the most coherent generating units is proposed which exploits a decision tree (DT) algorithm based on synchronized data measured by phasor measurement units (PMUs). Such DTs should be trained so as to provide accurate decisions for almost all possible disturbances in power systems. Three probabilistic parameters are, therefore, taken into account in a training stage including fault type, fault location, and the system load level at the time fault occurs. In order to generate the training dataset, different scenarios are simulated and appropriate attributes are extracted from voltage phasors. Furthermore, the most coherent generating units, which are the DT target, are determined in each scenario by evaluating the similarity between the frequency components existing in their speed variation signals. The effectiveness of the suggested approach is verified by its application to the 68-bus, 16-machine test system through which it reveals high accuracy in recognizing the most coherent generating units under various prevailing conditions in power grid.

A Synchrophasor-Based Decision Tree Approach for Identification of Most Coherent Generating Units

Inter-area oscillations in power systems needs to be monitored and accurate estimation of frequency and damping of the oscillations in real time is vital as they provide considerable insight into system stability. Zolotarev polynomial based filter bank (ZPBFB) is proposed for decomposing the PMU signals into monocomponents. Modal frequency and damping are obtained from the decomposed monocomponent signals through eigen realization algorithm (ERA). The salient property of ZPBFB lies in signal decomposition with sum of decomposed signals produced by filter bank, without oversampling, is an exact replica of input signal with delay. A narrow bandwidth of 0.1 Hz is achieved in the frequency range of 0 to 1 Hz with ZPBFB. The robustness of the proposed method to noise is demonstrated using Monte Carlo simulations. The proposed ZPBFB and cosine modulated filter bank (CMFB) are implemented on two-area test system and 16-machine, 68-bus system and the performances are compared. The efficacy of the proposed method is also demonstrated on three real time signals recorded by wide area frequency measurement system (WAFMS) at 3 locations in India on November 30, 2011.

Identification of Inter-Area Oscillations Using Zolotarev Polynomial Based Filter Bank With Eigen Realization Algorithm

Abstract A method is proposed to identify the generators participating in an inter-area mode and the coherent generators by analyzing the discrete signals from phasor measurement units (PMU). In the present work, Zolotarev polynomial based filter bank (ZPBFB) is adopted to decompose the measured signals into monocomponents for modal frequency and damping. ZPBFB is preferred due to the narrow bandwidth of 0.1 Hz and hence closely spaced modes can be distinguished. All the generator speed signals are analysed with ZPBFB to identify the generators participating in an inter-area mode. Principal component analysis (PCA) is widely used for clustering sampled data. It is proposed in the present work to apply PCA on the decomposed signals, obtained from ZPBFB, of those generators participating in an inter-area mode for generator coherency. The efficacy of the proposed method is demonstrated on IEEE two-area test system, 16-machine, 68-bus system and on real time signals recorded by wide area frequency measurement system (WAFMS) in India on November 30, 2011. The detailed simulation results are presented. The performance of the proposed method is compared with small signal stability analysis and wavelet phase difference (WPD) approach.

Generator Coherency Using Zolotarev Polynomial Based Filter Bank and Principal Component Analysis

Abstract Controlled islanding is an effective way of preventing the system from catastrophic blackouts. This is generally solved either as a constrained combinatorial optimization problem or a slow coherency based linearized approach. The combinatorial explosion of the solution space of an extensive power network increases the complexity of solving, while the linearized slow coherency approach cannot track the varying coherent generator groups with a change in system operating conditions. Offline coherent studies are utilized in wide area measurement system (WAMS) data-based approaches to determine islanding boundaries. So, the present study proposes a novel coherency based controlled islanding technique that clusters generators and load buses simultaneously from the measured signals, ensuring generator coherency. Therefore, identification of inter-area modes from bus voltage angle signals is necessary to determine coherent bus groups. So, Zolotarev polynomial based filter bank (ZPBFB) is adopted in the present work to determine inter-area modes. The dimensional reduction techniques are used to cluster the coherent buses. The bus clusters thus obtained with the proposed method are compared with bus clusters determined from small signal stability analysis. The proposed method is demonstrated on IEEE 39-bus, 68-bus and 118-bus test systems and compared with graph spectra based controlled islanding.

Detection of coherent groups using measured signals, in an inter-area mode, for creating controlled islands to protect the power system from blackout

In the present work, coherent generators participating in an inter-area mode are identified from time synchronized phasor measurements. In the first step, Zolotarev polynomial based filter bank (ZPBFB) is applied on the real power data of all generators obtained from phasor measurement units (PMUs) to decompose the given signal into monocomponents. The mono-components are then given to eigen realization algorithm (ERA) for modal frequency and damping. The decomposed signals of those generators participating in a particular inter-area mode are given to principal component analysis (PCA) for identifying generator coherency. The proposed method is demonstrated on IEEE two-area test system.

Coherency of generators for inter-area modes using digital filter bank and principal component analysis

Inter-area oscillations can be identified by analyzing the signals obtained from Phasor Measurement Units (PMUs) dispersed across the power network. In this paper, signals obtained from PMU are decomposed into monocomponent signals by a filter bank based on Zolotarev polynomials. A narrow bandwidth of 0.1 Hz is designed in the range of 0 to 1 Hz using Zolotarev polynomial based filter bank. Modal frequency and damping are estimated from the decomposed monocomponent signals through Eigen Realization Algorithm (ERA). The proposed method is demonstrated on two-area test system and the simulation results are presented.

http://ieeexplore.ieee.org/iel7/7368725/7380859/07380976.pdf

Kalyani Mandadi

Papers

Identification of Inter-Area Oscillations Using Zolotarev Polynomial Based Filter Bank With Eigen Realization Algorithm

Generator Coherency Using Zolotarev Polynomial Based Filter Bank and Principal Component Analysis

Detection of coherent groups using measured signals, in an inter-area mode, for creating controlled islands to protect the power system from blackout

Coherency of generators for inter-area modes using digital filter bank and principal component analysis

Identification of inter-area oscillations using Zolotarev filter bank with eigen realization algorithm