A reduced order method for swing mode eigenvalue calculating based on fuzzy coherency recognition

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.

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

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

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.

In this paper, a new approach for detecting realtime matching for controlled islanding based on the correlation coefficient of generators is proposed. In this approach, by online measuring generators rotor angle oscillations, the correlation coefficients between all pairs of generators are evaluated. Based on the evaluated correlation coefficients, coherent groups of generators are identified. Then, using correlation coefficient of coherent groups, splitting indices between coherent groups are evaluated. Following a disturbance, in the case of the risk of splitting coherent groups towards islanding, the values of splitting indices start to increase detecting tendency of coherent groups towards splitting state. The proposed approach is demonstrated on the IEEE 39-bus system with promising results.

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.

In this paper, on the basis of coherent grouping, fuzzy clustering method is used to study the effect of failure on coherent grouping. First this article use fuzzy clustering method to get a coherent classification result, use fuzzy clustering formula as a starting point, explore what factors will change before and after the fault, and then use the BPA program developed by the Chinese Academy of Electric Power to experiment on the IEEE39 node. As a result, it is obtained that the fault has a greater influence on the mutual admittance between generators and thus affects the grouping result.

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

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.

Optimized virtual inertia of wind turbine for rotor angle stability in interconnected power systems

A method of calculation of dynamic equivalents for simulation of large power systems has been developed and integrated in Hypersim power system simulator. The calculation of dynamic equivalent is performed by a Matlab program. It starts with the partitioning of the power system in groups of coherent generators using a coherency-based technique. Each coherent group is then aggregated to an equivalent generator and load buses are eliminated. The grouping algorithm is based on the structure preservation of detailed models of generators and their control. The integration of the dynamic equivalents calculation and Hypersim consists of interfacing these two tools via the generated EMTP file since Hypersim can export-import this format. Simulations tests applied to both full and reduced 10 machines, 39 bus power system confirm the validation of the calculation of dynamic equivalents and the integration procedure.

In this paper, a new approach for recognizing coherent groups of generators based on the correlation characteristics of generator rotor angle oscillation is proposed. In this approach, by online measuring generators rotor angle oscillations, the correlation coefficients between all pairs of generators are evaluated. Based on the evaluated correlation coefficients, coherent groups of generators are identified. Comparing the result with time domain transient stability simulation following a disturbance and also modal analysis, leads to similar coherent groups. The proposed approach is demonstrated on the IEEE 39-bus system with promising results.

Multichannel continuous wavelet transform approach to estimate electromechanical oscillation modes, mode shapes and coherent groups from synchrophasors in bulk power grids

A reasonable islanding strategy of a power system is the final resort for preventing a cascading failure and/or a large-area blackout from occurrence. In recent years, the applications of wide area measurement systems (WAMS) in emergency control of power systems are increasing. Therefore, a new WAMS-based controlled islanding scheme for interconnected power systems is proposed. First, four similarity indexes associated with the trajectories of generators are defined, and the weights of these four indexes are determined by using the well-developed entropy theory. Then, a coherency identification algorithm based on hierarchical clustering is presented to determine the coherent groups of generators. Secondly, an optimization model for determining controlled islanding schemes based on the coherent groups of generators is developed to seek the optimal cutset. Finally, a 16-generator 68-bus power system and a reduced WECC 29-unit 179-bus power system are employed to demonstrate the proposed WAMS-based controlled islanding schemes, and comparisons with existing slow coherency based controlled islanding strategies are also carried out.

Inter-area oscillations and cascading failures are the most serious threats to the security of the electric power system. Uncontrolled islanding will occur in the event of an unstable inter-area oscillation or a progressive cascading failure. The establishment of uncontrolled islands with a deficiency in load-generation balance is the main reason for system blackout. Controlled islanding has been proposed as a preventive strategy for reducing the risk of blackout in this regard. A new algorithm for applying the controlled islanding strategy is proposed in this paper, based on load coherency and nearest electrical distances between coherent groups of generators. Coherent generators as the main core for controlled islands are identified in this method, which is based on the correlation coefficients between generators and the DBSCAN clustering algorithm. The sub-networks are then created by applying mixed-integer linear programming to each coherent group. Once this is accomplished, non-linear programming is used to construct the stable sub-networks associated with islands that meet the requirements of load-generation balance, voltage limitations, and transmission limits. The proposed scheme is implemented on the small-scale IEEE 39-bus system and a large-scale realistic power system which is the Iran power grid. The results demonstrate that the proposed method is capable of being implemented in a real power system.