Coherent Groups Of Generators Research Articles

A Predictive Out-of-Step Identification Method Based on Wide-Area Velocity-Acceleration Trajectory

Online identification of the transient instability is essential in power system analysis, as it facilitates the grid operator to decide and coordinate system failure correction control actions. The rapid technological development of phasor measurement units (PMUs) provides an opportunity for online transient out-of-step identification in real-time. This paper analyzes the characteristics of angular velocity-acceleration trajectories in detail, and then proposes a predictive transient out-of-step identification method among coherent groups of generators based on the velocity-acceleration trajectory sampled by PMUs. To prove that the method can distinguish the transient out-of-step event following disturbance reliably, a mathematical proof of the criterion extended to multi-machine system is made, and the influences of the exciter and emergency control are taken into consideration to a certain extent. Finally, the effectiveness and predictability of the proposed method are demonstrated on a 39-bus New England test system and two large-scale power system simulation models of China by numerical studies.

Design of a decentralized wide-area control system to tolerate the loss of communication paths based on coherency analysis

The objective of this article is to design a wide-area control (WAC) system based on coherent groups of generators to overcome the loss of data and communication failures. In the conventional approach, the wide-area controller is designed by employing the state feedback control. It requires all the states on the controller input side and sending signals to all generators’ excitation systems on the output side. Therefore, good communication paths are necessary to get quality data every time. In addition, there should not be any data loss or false data due to phasor measurement units (PMUs) installed at every generator. However, it is not possible to get quality data without losing anything. Hence, it leads to the unstable condition of the entire power system. To address this problem, the area-wise coherency-based WAC system is proposed in this article to overcome the above-mentioned problems. The entire power system is divided into different areas based on coherency. Therefore, the centralized WAC system is divided into decentralized WAC systems based upon coherent groups of generators. This can be achieved by employing structurally constrained H2-norm optimization. Moreover, to validate the strength of the proposed controller, it is compared with different existing controllers reported in the literature by considering different case studies on IEEE-39 and IEEE-68 bus systems.

Development of Systems Energy Research: N.I. Voropai’s Contribution

The scientific works of N.I. Voropai in the systems research methodology of power engineering, including the key properties of electric power systems (EPSs) and development of their analysis methods, are analyzed. Special attention is paid to EPS reliability, survivability, stability, and cybersecurity. A scheme of cause-and-effect relations between various types of stability violations, including new ones, that occur in modern EPSs as a result of the ongoing innovation-technological transformations in them, and the corresponding states of EPS, is drawn up. The studies of EPS heterogeneity during transients, which manifest themselves as different degrees of coherence in the motion of generators, are considered. These studies are aimed, in particular, at identifying the groups of coherent generators and revealing weak cut sets between them. The studies of EPS dynamic properties and mechanisms governing the development of large-scale cascade accidents in the power systems of Russia are analyzed. Methods and systems for control of power system operation modes, including those when the power system properties undergo transformation, as well as emergency control methods and systems, are considered. A methodology and models for substantiating the development of power systems under modern conditions, as well as energy security analysis and assurance methods are presented. The prospects for development of interstate electric links between the Unified Energy System of Russia and the power systems of neighboring countries, and for the formation of Eurasian and Global super-grids are considered, and the main results obtained in the field of establishing integrated multi-energy systems are given. The invaluable contribution N.I. Voropai has made in the development of systems studies in power engineering and its importance for applied problems is shown.

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

This paper presents online criteria of frequency stability-based controlled islanding scheme based on the density-based clustering algorithm and the system electrical distance from wide-area measuring system (WAMS) data. For this issue, at each time window, based on evaluating the correlation coefficient criteria between all two pairs of synchronous generators (SGs), the possible coherent generator groups are identified. Next, by using clustering algorithm, the identified coherent SGs are clustered and sorted through online working mode. In this case, an online index is proposed which and developed index in the form of mixed-integer linear programming (MILP) which through real-time evaluations, the system frequency stability is evaluated. In the use of MILP processes, the network electrical distance is considered as the main objective function (OF) which by solving the proposed OF through minimum criteria, proper islanding locations are identified. Finally, at the post-islanding period, a nonlinear programming (NLP) procedure is performed through each individual islands where corresponding proper operational feasibilities are satisfied. In this case of transient stability criteria, based on the coherent SGs located at each island, the stability criteria is preserved through coherent groups as the core of islands. Also, by allocating proper load buses based on the SGs governor capacities, the islands frequency stabilities are controlled through acceptable regions. The effectiveness of the proposed scheme is carried through two IEEE 39-bus test system and Iran practical power grid. Results present the effectiveness of the proposed approach through different islanding conditions.

An Efficient Rapid Method for Generators Coherency Identification in Large Power Systems

With steadily increasing interest in enhancing large power systems’ transient stability, generator coherency identification has become critical for the dynamic equivalents, controlled-islanding, and wide-area control of these systems. This paper presents an approach based on two classical and powerful techniques. The proposed method comprises the slow coherency method followed by the time-domain-based simulation of transient stability to identify the coherent groups of generators. In this regard, various operating conditions of the system are considered to obtain the updated coherency information between groups of generators by analyzing the chosen generator rotor angle. The proposed approach’s merits are tested on the New England IEEE 39-Bus and modified IEEE 118-Bus test systems in the PowerFactory software tools through Python. Corresponding simulation results validate the proposed paradigm’s effectiveness by enhancing the transient stability speed of a large power system without decreasing its coherency behavior accuracy. It is also observed that the proposed scheme tends to be more consistent in determining the coherent groups of generators in the presence of disturbances and different operational conditions.

Real-time transient stability prediction and coherency identification in power systems using Koopman mode analysis

In this paper, we propose a novel methodology based on Koopman mode analysis to predict the transient stability of a power system in real-time. The method assesses the stability of the system based on a sliding sampling window of PMU measurements, and it detects the evolving instabilities by predicting future samples and investigating the computed Koopman eigenvalues. This approach is also able to identify alarm conditions, which include slowly evolving instabilities that may not be detected by predicting future samples in a limited time horizon. Identifying these conditions provides additional time to prepare a proper set of emergency control actions to be performed when necessary. Using the proposed method, groups of coherent generators that play roles in the evolving instabilities can also be identified, contributing to the design of a defensive islanding scheme for unstable cases. The efficacy of the proposed approach is demonstrated by simulating its performance with three test systems of different scales.

A new intelligent time series prediction technique for coherency identification performance enhancement

The manuscript proposes a time series prediction technique to enhance the response time of the coherency identification technique. The proposed methodology utilizes the nonlinear autoregressive exogenous neural network (NARX) algorithm to predict the generator speed deviations following a disturbance in the system. Consequently, the coherency identification technique based on independent component analysis (ICA) is utilized on the predicted system responses. The effectiveness of the proposed approach is demonstrated on of the IEEE 16-generator 68-bus test system model. The result shows that the proposed technique can predict 0:2s following a disturbance in the system accurately. Therefore, the NARX allows a 0:2s head start for the ICA to determine the real-time coherent generator group in the system. Furthermore, the result shows that the proposed approach can identify the coherent group of generators based on the predicted generator speed deviation in all cases considered in this study, accurately. Conclusively, the result implies that the proposed technique can speed up the overall coherency identification process in a power system operation.

Identification of coherent groups of generators for out-of-step protection using online measurements

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

Optimal controlled islanding considering frequency‐arresting and frequency‐stabilising constraints: A graph theory‐assisted approach

The optimal controlled islanding of power systems is a practical solution to prevent system blackouts if the boundary of islands and corrective control actions in each island are carefully specified. This paper establishes a model for the controlled islanding problem by a proposed mixed integer linear program (MILP). The frequency-arresting (FA) and frequency-stabilising (FS) constraints are linearised and incorporated in our FA-FS-constrained MILP model to prevent triggering of load shedding (LS) relays. This achievement makes our model capable of handling low-inertia networks. Intentional LS and stepwise generation curtailment are corrective actions accommodated for frequency control and power mismatch considerations. These corrective actions increase the degree of freedom and broaden the feasible space of the MILP model under the envisaged tough operating conditions. Our model's computational efficiency is improved using a proposed graph theory-based network reduction technique. The basic groups of coherent generators, determined in the offline mode, are aggregated as equivalent buses using an extended Steiner tree method. A graph-path determination technique is also proposed to generate disconnection constraints (between equivalent buses of incoherent areas) and bus-allocation constraints. Simulation results on the IEEE 39-bus test system and a 76-bus case study verify the proposed network reduction technique's effectiveness and the MILP model.

Identification of Low-Frequency Oscillation Modes Using PMU Based Data-Driven Dynamic Mode Decomposition Algorithm

Power system inter-area oscillations curtail the power transferring capabilities of the transmission lines in a large interconnected power system. Accurate identification of dominant modes and associated contributing generators is important to avoid power system failures by taking appropriate remedial measures. This paper proposes a multi-channel Improved Dynamic Mode Decomposition (IDMD) algorithm-based modal analysis technique using Synchrophasors measurement. First, a reduced-order dynamic power system model is estimated and using this model dominant oscillation modes, corresponding modes shapes, damping ratio, coherent group of generators, participation factors are determined. To improve the accuracy data stacking technique is used to capture detailed information of the system. An optimal hard threshold technique is utilized to select the most optimal model order to avoid uncertainties due to the presence of high level of measurement noise. The study results show that the proposed algorithm gives an accurate and robust solution even in systems having high level of noise in the measurement data. The performance of the proposed technique is tested on simulated data from two-area four-machine system and wNAPS 41-bus 16-generator system with PMU measurements corrupted with different levels of measurement noise. To further strengthen the viewpoint, the proposed method is validated on real-time PMU measurement from ISO New England data to validate the accuracy of the proposed work.

Controlled Islanding Based on the Coherency of Generators and Minimum Electrical Distance

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.

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

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.

Distributed Optimal Control of Transient Stability for a Power Information Physical System

The development of power energy structures and information communication technology has promoted the renewal of smart grid information-physical structures. At the same time, the changes in the smart grid energy structure and the vulnerability of the information network threaten the stability of the power system and uses multiagent control theory to improve the transient stability of the power grid which has strong practicability. In this paper, an optimized distributed control scheme is proposed for application to the smart grid model so that the grid system can flexibly adapt to the external operating conditions and recover to stable operating conditions after being disturbed. In this paper, an intelligent power grid information-physical network simulation system is established. According to the information exchange within the multiagent system, groups of coherent generators in the disturbed power grid in different regions are identified and controlled. Distributed control is applied to maintain the exponential frequency synchronization and phase angle aggregation of the synchronous generators to achieve transient stability. Finally, the effectiveness and rapidity of the proposed distributed optimal control scheme are verified by simulation analysis of the IEEE 39 node model.

Transient Instability Mitigation via Repetitive Corrective Actions Based Upon the Real-Time Macrocoherency Evaluation

The objective of this article is to develop a control mechanism that can suppress the ongoing transient instability in a power system through the continuous delivery of corrective actions. The continuous delivery of corrective actions basically refers to performing multiple adjustments in series until the transient instability is completely healed. The motivation behind the particular work arises from the observation that exercising the corrective action merely once may not always be sufficient to restore the stability of the system. In order to enable the continuous delivery of corrective actions, a macrolevel dynamic assessment approach is taken instead of the conventional and microlevel static coherency assessment. The macrolevel coherency assessment is to be carried out based upon real-time snapshots of system quantities for the on-spot identification of generator groups that have grossly lost synchronism with one another. A complete transient instability mitigation program is developed with due application of the macrocoherency assessment in delivering continuous corrective actions. In order to ensure almost instantaneous computation of coherent generator groups, a novel clustering algorithm is also developed. Corrective actions are delivered in the form of generator rejection and load curtailment. Extensive verification of the proposed methodology is carried out by means of real-time software-in-loop simulations.

Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources

With the fast growth of renewable energy sources (RES), more and more uncertainties are involved and influencing the stable operation of power systems. Controlled islanding is the last measure to prevent power system blackouts, thus this paper aims to propose a novel model of system separation based on Online Coherency Identification and Adjustable Robust Optimization Programming (OCI-AROP) for minimizing load shedding considering the uncertainties of RES. First, Fuzzy C-Means (FCM) clustering method with F -statistics is utilized to identify the coherent generator groups with the frequency data measured by Phasor Measurement Units (PMUs). Then, the OCI-AROP model considering coherent group constraints, connectivity constraints and robustness constraints about RES are presented. Finally, the case studies on IEEE-39 bus system and WECC-179 bus system are employed to demonstrate the effectiveness of the proposed OCI-AROP model, and comparisons among the OCI-AROP model and the other models are also given to show its superiority.

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

In the power system with virtual inertias, the current dynamic analysis of frequency stability is insufficient to ensure grid security because the virtual inertia control also has a significant effect on the first swing stability of the rotor angle. This paper investigates a novel virtual inertia control strategy for wind turbines to improve the first swing stability of the rotor angle in the interconnected power grid. The virtual energy provided by the virtual inertia control of the wind turbines is calculated. Considering that the rotor angle swings in the forward and backward directions, the effect of virtual energies in two different coherent generator groups on system transient energy conversion is evaluated. Moreover, a novel control method is proposed to provide more reliable inertia support. A typical two-area interconnected power system with a high wind penetration of 35%, which consists of four conventional power plants and two doubly fed induction generator (DFIG)-based wind farms, is simulated. The simulation results demonstrate that in the transient events, the rotor angle difference between the coherent generator groups can be reduced by regulating the variable inertias, significantly improving the reliability of the virtual inertia support of wind turbines.

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

In power systems, electromechanical oscillatory dynamics, dominant modes, mode shapes, participation factors and coherent groups of generators are all important modal parameters. Most of the existing measurement-based methods focus on estimating one or two aspects among dominant modes, mode shapes, participation factors, and coherent groups. Nevertheless, none of existing methods explore all the four aspects from synchrophasor measurements at the same time. In this work, an eigensystem realization algorithm (ERA) based data-driven approach is developed to estimate dominant modes, mode shapes, participation factors and coherent groups from synchrophasor measurements in a holistic framework. In the proposed approach, the reduced power system dynamic model is first estimated by ERA from multichannel synchrophasor measurements. Then, based on the estimated power system dynamic model, electromechanical oscillation modes, corresponding mode shapes and the left eigenvalue vectors are solved. Next, using the solved mode shapes and left eigenvalue vectors, the participation factors of the generators associated with the electromechanical oscillation modes are calculated. Further, the direction cosines among the generators representing coherent strength of generators are calculated by using the achieved mode shapes. Finally, in accordance with the calculated direction cosines, the coherent generators are then identified. The proposed approach is demonstrated with the simulation data of the 16-machine, 68-bus test system and the field Phasor Measurement Units (PMUs) data collected in Liaoning Electric Power Grid. The results demonstrate that the proposed data-driven approach captures dominant modes, mode shapes, participation factors, and coherent groups of generators from synchrophasor measurements with high accuracy and efficiency.

A Quantile Regression-Based Approach for Online Probabilistic Prediction of Unstable Groups of Coherent Generators in Power Systems

This paper proposes a novel framework for probabilistic data-driven prediction of unstable groups of coherent generators in interconnected power systems. In contrast to existing techniques in which deterministic classification or forecasting approaches are applied to an offline database, the current study relies on a prediction interval (PI)-based method to tackle prediction uncertainties. First, similarity coefficients (SCs) are considered as internal outputs and calculated for all offline cases. Then, at some generator terminals as selected via a feature selection process, voltage values are measured and used as the input features of the prediction tool. Quantile regression forest is conducted to generate PIs, in which several intervals with certain probabilities are predicted for SCs between any pair of generators. Thereafter, the obtained PIs are used to shape an empirical cumulative distribution function of SCs; a Monte Carlo simulation is then conducted to find a reliable estimate of possible grouping patterns. Finally, a decision-making phase is employed to draw clear distinctions among various parts of the most plausible grouping pattern with respect to a reliability index. This approach can offer power system operators wider flexibility to select a corrective control strategy. The effective performance of the developed approach is demonstrated on several IEEE test systems, followed by a discussion of results.

Coherency detection of generators using recurrence quantification analysis

A novel model-free method for detecting coherency of generators in power systems by means of the Wavelet Packet Decomposition (WPD) and the Recurrence Quantification Analysis (RQA) is developed. The time-series rotor angles of generators are used in this approach to detect coherent group of generators. Noise is a critical issue that needs to be considered when evaluating the performance of the coherency detection methods based on monitored data. Coherent groups of generators are detected under different disturbances and the results appear to be robust in the presence of noise. Simulation data from a 29-machine 179-bus system model are applied and results based on this method presented.

Intentional islanding method based on community detection for distribution networks

Complex network theory is introduced to solve the islanding problem in an emergency of distribution networks. In this study, the authors put forward an intentional islanding method based on community detection. In this method, a new index has been defined called electrical edge betweenness, on the strength of edge betweenness in complex networks, which fuses electrical characteristics with topological features of actual power lines. Based on the index, the Girvan–Newman algorithm is employed to detect the community structure of distribution networks. Through referring to the modularity value (function Q) and coherent generator groups, they can get a reasonable amount and regions of communities. Then the whole distribution network can be partitioned into several self-sustainable islands meeting the stable operation constraints. The effectiveness of the authors’ proposed method is tested on a standard IEEE 118-bus system.