A coherent generator group identification algorithm under extreme conditions

The development of the modern Global Positioning System (GPS) technique makes the real time monitoring of the generator status workable. However it is impractical and unnecessary to install GPS devices at all generators due to the high cost. Observations and calculations indicate that most of the generators in a large power system have some similar transient power angle characteristics. Therefore the generators can be grouped according to the similarity of their power angle characteristics, and within each group only one generator is required to install GPS device to monitor the real time status of all the generators in the group. Based on the coherence group theory, the coherence groups of the generators are irrelevant to the magnitude of disturbance and the details of the generator unit model. Therefore, by building a classical synchronous generator linear system model, an accessible Gram matrix model can be derived. Then according to the generator identification rule of ε-coherence (or coherent group), the generator coherence groups can be identified in a large power system. The methodology can facilitate the real time monitoring of generators' status and the real-time control based on the GPS technique.

This paper presented a reduced-order method for swing mode eigenvalue calculating based on fuzzy coherency recognition. First, we recognize the coherent generator groups using the fuzzy clustering method. Then we aggregated the generators in a coherent group into a single equivalent generator that the dimension of the state equation reduced evidently. Using QR algorithm to the reduced-order state equation we calculated the eigenvalues of the inter-area mode. The eigenvalues of local mode calculated by using QR algorithm to the sub-state matrices corresponding to the coherent groups separately. Thus, all eigenvalues of swing mode can be calculated. We have given detailed results of both the coherent generator groups recognition and the eigenvalues calculating of the 10-machine New England power system. The results shows that the method for eigenvalue calculation is simple and practical.

Identification of coherent generators groups in multi-machine power systems is an important step for both operation and control needs. This paper presents a new identification method based on the generators' rotor angular positions. The methodology consists of extracting the time-domain dynamic responses of generators to build a relationship matrix indicating the degree of coherency between any pair of generators. Then, by applying the Fuzzy C Mean (FCM) clustering, coherent groups are determined. The Centre of Inertia (COI) is used to represent the coherent group in order to visualize the global oscillations of the rotor angle of that particular group following a disturbance. Applications on the 10-machine New England power system show the feasibility and the validity of the proposed methodology.

An analytical method to decompose a power system into a number of weakly interconnected areas and further to identify the coherent machine groups in each area is presented in this paper. The proposed method is based on a technique of grouping related elements of the network. The system decomposition and coherency groupings obtained is independent of the location of the faults to be simulated in the system. The first part of the algorithm of decomposing the system into weakly coupled areas uses only the information about the transfer admittances between the generator internal nodes. For the subsequent part of identifying the coherent groups of machines in each area, transfer admittances and moments of inertias of the machines are used.

Frequency stability-based controlled islanding scheme based on clustering algorithm and electrical distance using real-time dynamic criteria from WAMS data

Since phasor measurement units (PMU) were invented, there has been growing interest in developing methodologies for improving monitoring, protection and control of power systems in real time. In this study, the authors propose a new methodology, based on graph modelling, to identify coherent groups of generators in a real‐time fashion. The coherent groups are identified with instantaneous values measured from the system through PMUs, and the methodology needs neither setting the number of desired groups nor defining a threshold value since it is based on coupling factors between generators. Moreover, it is proposed a new method for the online definition of areas for islanding when this action is required as the latest emergency control method. The methodology assigns the non‐generation buses to the previously found generators coherent groups considering three criteria: electrical distance to the group of generators, topology, and operational constraints, which are verified by mean of an optimal power flow. The methodology is tested on the IEEE 39‐bus and IEEE 118‐bus test systems. Results show that the real‐time identification of coherent groups and the definition of areas allow the development of islanding strategies with promising results.

Social identity shapes stress appraisals in people with a history of depression

An eigensystem realization algorithm based data-driven approach for extracting electromechanical oscillation dynamic patterns from synchrophasor measurements in bulk power grids

Real time monitoring and control of low frequency oscillations is an important issue to be taken into consideration in modern interconnected power systems operation. A method to find the system small signal stability and coherent generators following a disturbance in real-time is proposed in this work The synchronously sampled data available from Phasor Measurement Units (PMUs) at a high sampling rate provides an opportunity to observe the system operation near to real-time. The first four cycles of post disturbance data comprising of bus voltage magnitudes and angles is measured from optimally placed PMUs. This data is fed to different Artificial Neural Networks to determine the damping index and coherent groups. The dimensionality reduction is performed using Principal Component Analysis. The suggested method is very fast and accurate in predicting the system damping and coherent groups in real-time for different operating conditions including topological variations and 3-phase faults. The efficiency of the proposed methodology is investigated on IEEE 39-bus test system.

Fast and accurate identification of coherent generator groups is helpful in dynamic and transient stability analysis as well as other applications such as controlled islanding. In this study, a new method is presented for predicting the generators’ grouping scheme based on the data measured before and in a short time after the disturbance occurrence. To do that, a classifier model is trained using a training dataset. In the training dataset, the input is the attributes, which are obtained directly or indirectly from the data measured by phasor measurement units. On the other hand, the target in the training dataset is the generators’ grouping, which in this study is calculated using a new method called subtractive technique. In subtractive technique, coherent generator groups are determined based on the generator density values. When the classifier model is built using the training dataset, it can be used for online applications. In this study, the well‐known 68‐bus, 16‐machine power system as well as the Iranian 400 and 230 kV south east regional grid are used as the test systems for investigating the efficiency of proposed coherent group prediction method. Results show that the proposed method can predict the generators’ grouping scheme with high accuracy.

The coherent generator groups identified method was proposed via Empirical Mode Decomposition (EMD) and Stochastic Subspace Identification (SSI) method in this paper. Only the generator rotor speed gathered from the Wide Area Measurement System (WAMS) is used in the proposed method, and the detailed model and parameters of power system components are not needed. And the phase diagram obtained by using the SSI was employed in the proposed method to identify the coherent generator groups. At the end, the simulation was tested on the CEPRI system with 8-generator. The results of test system testify the efficiency of the proposed method.

A new approach for online coherency identification in power systems based on correlation characteristics of generators rotor oscillations

Power system oscillation monitoring and control in real-time is an important issue to be taken into consideration in modern interconnected power systems operation. This paper proposes a method to find the system damping and identification of coherent groups in the system following a disturbance in real-time using Artificial Neural Network. The first four cycles of post disturbance data comprising of bus voltage magnitudes and angles measured from optimally placed Phasor Measurement Units using Integer Linear Programming. The dimensionality reduction is also done using Principal Component Analysis. The results show that the proposed method is very fast and predict the damping and coherent groups accurately in real-time for all operating conditions including topological variations, with very less computational burden. The effectiveness of proposed approach is tested on IEEE 39-bus test system.

Situational awareness of coherency behavior of synchronous generators in a power system with utility-scale photovoltaics

A coherent groups recognition method using fuzzy clustering method based on A-K networks is proposed. Firstly, a fuzzy similarity matrix is formed by applying maximum-min-mum algorithm. Then the A-K networks are trained with each row of the fuzzy similarity matrix as inputs. The nerves of output layer which win ultimately represent different dynamic styles. Finally, it is tested on the EPRI-36 bus model of PSASP. The results based on A-K fuzzy method are more similar to the results based on time simulation compared to A-K method. Moreover, A-K fuzzy method can identify coherent generator groups in greater time range.