Power system low frequency oscillations monitoring and generator coherency determination in real time

Power system low frequency oscillations' monitoring is a critical issue in operation of modern interconnected power system. The main reason behind it is that the poorly damped low frequency oscillations can occur under stressed conditions and can lead to system break-up or blackout. The modern utilities employ real-time wide area based monitoring using Phasor Measurement Units (PMU). So, the optimal PMU placement is the most pertinent aspect to be taken care of keeping an eye on purpose of deployment and system characteristics. This paper addresses this problem keeping in mind both the system's topological aspects and its behaviour. A multi-criteria contingency constrained approach is proposed for optimal PMU placement for real-time wide area low frequency oscillation monitoring. The buses critical to oscillation point of view are obtained using different criteria and then included in an Integer Linear programming approach. The proposed approach takes care of complete system observability and considers single line outage and PMU loss. The proposed method is applied on IEEE-39 bus system and findings are shown. The proposed scheme, however, is not limited to present application and any number of practical criteria can be used in this generalized approach to evaluate PMU locations for different applications.

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.

Estimating the low-frequency oscillation in an interconnected power system is the most important requirement to keep the power system in a stable operating condition. This research work deals with a hybrid robust and accurate approach using a combination of Estimation of signal parameters via rotational invariant techniques (ESPRIT) and Prony algorithm to extract the low-frequency oscillatory modes present in the power system. The observation inspires the hybrid method that the true modes of the signal are present in any signal processing technique (for example, Prony algorithm) along with other fictitious modes regardless of the order of the power system. Moreover, this research obtained true modes by calculating Euclidean distance and applying the threshold value concept. The proposed technique is tested with different noise conditions and varying sampling rates of Phasor Measurement Unit (PMU) to check the proposed hybrid technique’s robustness compared to Prony and the multiple ESPRIT method. Finally, the proposed method is applied to the real signal obtained from the Western Electricity Coordinating Council (WECC) network, and it estimates accurate and precise parameters compared to other methods. The accuracy for estimation of frequency and attenuation factor is calculated for the three-mode synthetic signal at a noise level of 10dB by the hybrid algorithm, multiple ESPRIT, and Prony algorithm, which shows that hybrid algorithm has minimum percentage error. Thus, the proposed hybrid algorithm accurately estimates the parameters of low-frequency oscillation as compared to other existing methods without involving any fictitious modes.

Low frequency oscillation in power system may cause system failure and blackout. It is possible to avoid low frequency oscillation if we can detect the oscillation mode with low damping in early stage. This paper presents the Frequency Domain Decomposition (FDD) method, which can extract the low frequency oscillation modes from ambient measurement data of Phasor Measurement Units(PMU). The parameters of mode shape from poorly damped oscillation modes, such as damping ratio and modal frequency, can be directly determined from ambient PMU measurements. Then this paper presents the implementation of oscillation mode identification application with OpenPDC platform, which is an open-source platform and provides an easy-to-use test platform for phasor measurement application. The case study shows that the implementation of Oscillation Mode Identification module with FDD method and OpenPDC platform is very efficient for processing large quantity of PMU data and identify low frequency oscillation mode, which is key to take early actions avoiding low-frequency oscillation in power system.

A comprehensive review of wide-area protection, control and monitoring systems.- Introduction to WAMS and Its Applications for Future Power System.- Information and communication infrastructures in modern wide-area systems.- Wide-Area Monitoring Systems and Phasor Measurement Units.- Optimal selection of Phasor Measurement Units.- Coordinated designs of fuzzy PSSs and load frequency control for damping power system oscillations considering wind power penetration.- Wide-area monitoring of electromechanical oscillations on large interconnected power systems based on simultaneous processing spatio-temporal data.- Electromechanical mode estimation in power system using a novel nonstationary approach.- Small Signal Stability Improvement of Pumped Storage Hydropower using Wide Area Signal Considering Wind Farm.- Impact Analysis and Robust Coordinated Control of low frequency oscillations in wind integrated power system.- Frequency Stability of Two-Area Interconnected Power System with Doubly Fed Induction Generator Based Wind Turbine.- Wide-Area Measurement-Based Voltage Stability Assessment by Coupled Single-Port Models.- Adaptive WAMS-Based Secondary Voltage Control.- Applications of Decision Tree and Random Forest Methods for Real-Time Voltage Stability Assessment Using Wide Area Measurements.- Superseding Mal-operation of Distance Relay Under Stressed System Conditions.- Real-time voltage stability monitoring using machine learning-based PMU measurements.- Wide Area Transmission System Fault Analysis Based on Three-phase State Estimation with Considering Measurement and Parameter Errors.- Data-driven Wide-Area Situation Analyzer for power system event detection and severity assessment.- Techno-economic analysis of WAMS based islanding detection algorithm for microgrids with minimal PMU in smart grid environment.- Independent Estimation of Generator Clustering and Islanding Conditions in Power System with Microgrid and Inverter-based Generation.- Resilience in Wide Area Monitoring Systems for the Smart Grid.- Cyber Kill Chain-based Intrusion Detection Systems for Cyber-Physical Security in Smart Grid.

On-line measurement of low-frequency oscillations in power systems

The wind integration is on the rise in modern grids to generate cleaner energy due to increasing environmental concerns. This further escalates the problem of low frequency oscillations (LFOs) in power system. Thus, real-time monitoring of oscillations becomes even more important in present interconnected power systems. The conventional methods are offline and time consuming. In this work, a wide-area based method employing Phasor Measurement Units (PMUs) and Artificial Neural Network (ANN) is proposed to predict the system oscillatory status in real-time. The PMUs are optimally placed using modified Integer Linear Programming. The PMU data is dimensionally reduced using Principal Component Analysis before using it to train the ANN. The ANN predicts the LFO related information like damping ratio and frequency and an index to access the mode’s localness. The proposed methodology is verified on IEEE New England benchmark system. The suggested method is very fast and accurate in predicting the required information in real-time for different operating conditions involving topological variations with very less computational requirement.

With rapid proliferation of wind generation in current generation mix, the issue of low frequency oscillations (LFOs) may get escalated in the modern power grids. The eigenvalue and dynamic sensitivity analysis have been employed to examine the effect of wind integration on system damping. Further, a wide area based robust damping improvement control is suggested. It involves the coordinated control of power system stabilizers (PSSs) of synchronous generators (SGs) and power oscillation dampers (PODs) of doubly fed induction generators (DFIGs). The robust control is attained by employing a new fitness function based on eigenvalue and damping ratio and optimized by Whale Optimization Algorithm (WOA). The wide area POD inputs are selected using modal observability criterion, obtained using phasor measurement units (PMUs) located optimally in the system. The results are verified on IEEE benchmark 68 bus NY-NE (New York- New England) test system. The simulation results highlights the robustness of proposed control to changing system conditions and shows its effectiveness in augmenting system damping and thus small signal stability with high level of wind penetration.

The development of power networks has led to low-frequency oscillations in the power system. The relatively small and sudden disturbances in the network cause such oscillations in the system. In the normal case, the oscillations damp rapidly and the amplitude of oscillations does not exceed a certain value. But depending on the operating point conditions and the values of the system parameters, these oscillations may continue for a long time and at worst increase their amplitude. These oscillations can affect the power transmission capability and stability of the power system. PSS (Power System Stabilizer) and FACTS (Flexible AC Transmission Systems) devices are commonly used for damping low-frequency oscillations. The Interline Power Flow Controller (IPFC) among the FACTS device is suitable for the power flow control and stability of power systems. This chapter establishes an approach to derive the dynamical model of a multi-machine power system with embedded IPFC devices. Derivations about the stability analysis and the incorporating of IPFC to enhance the damping of low-frequency oscillations in a multi-machine power system based on Modified Phillips-Heffron modeling has been done. To demonstrate the application and efficiency of the developed models, a case study on a three-machine test power system with adding IPFC has been presented. Numerical results with MATLAB for dynamical simulations show the significant effects of IPFC on damping the low-frequency oscillations and especially validates the modeling procedure.KeywordsIPFCPhillips-Heffron modelFACTS DevicesStability of Multi-machine power systems

Model reduction techniques are often applied to large-scale complex power systems to increase simulation performance. The bottleneck of existing methods to get a high reduction ratio lies in: (1) Coherency identification is static and conservative. Some coherent generators are not detected when system topology or operating point changes. (2) Solitary generators outside any coherency group are not aggregated regardless of their importance. To overcome the first problem, a measurement-based online coherency identification method was used in this paper. By analyzing post-fault trajectories measured by phasor measurement units (PMUs), coherency generators were identified through principal component analysis. The method can track conherency groups with time-varying system topology and operating points. To address the second problem, sensitivity analysis was employed into model reduction in this paper. The sensitivity of tie-line power flows against injected active power of external system generators was derived. Those generators having minimal impacts on tie-line power flows were replaced with negative impedances. Case studies show that the proposed method can handle well these solitary generators and the reduction ratio can be enhanced. Future work will include generalization of the sensitivity method.

This paper proposes adding a controller to the energy storage system (ESS) to enhance their contribution for damping low-frequency oscillation (LFO) in power systems integrated with high penetration of different types of renewable energy sources (RES). For instance, wind turbines and photovoltaic (PV) solar systems. This work proposes superconducting magnetic energy storage (SMES) as an ESS. The proportional–integral–derivative (PID) and fractional-order PID (FOPID) are suggested as supporter controllers with SMES. The PID and FOPID controller’s optimal values will be obtained using particle swarm optimization (PSO) is used as the optimization method. Both local area and inter-area oscillation is considered in this work as a LFO. To investigate the impact of adding the SMES with the proposed controller, a multimachine power system with different integration scenarios and cases is carried out with a PV system and wind turbine. The system responses are presented and discussed to show the superiority of the proposed controller both in the time domain and by eigenvalues analysis.

Power system low frequency oscillation mode estimation using wide area measurement systems

Power system monitoring and control in real time is a challenging task for modern power system due to large operational constraints. The deployment of phasor measurement units (PMUs) at key locations provides an opportunity for devising effective power system monitoring and control measures. In this study, a new method is proposed to determine the real-time transient stability status and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through radial basis function neural network. The first six cycles of synchronously sampled post-fault data measurements from PMUs consisting of rotor angles and voltages of generators are taken as the input to the neural network to predict the future state of the system. The proposed method can also determine the synchronism state of the individual machine in real time. The proposed scheme is demonstrated on the IEEE-39 bus test system at different operating conditions.

This paper illustrates the development of an intelligent local area signals based controller for damping low-frequency oscillations in power systems. The controller is trained offline to perform well under a wide variety of power system operating points, allowing it to handle the complex, stochastic, and time-varying nature of power systems. Neural network based system identification eliminates the need to develop accurate models from first principles for control design, resulting in a methodology that is completely data driven. The virtual generator concept is used to generate simplified representations of the power system online using time-synchronized signals from phasor measurement units at generating stations within an area of the system. These representations improve scalability by reducing the complexity of the system "seen" by the controller and by allowing it to treat a group of several synchronous machines at distant locations from each other as a single unit for damping control purposes. A reinforcement learning mechanism for approximate dynamic programming allows the controller to approach optimality as it gains experience through interactions with simulations of the system. Results obtained on the 68-bus New England/New York benchmark system demonstrate the effectiveness of the method in damping low-frequency inter-area oscillations without additional control effort.

The objective of this paper is to theoretically investigate the application of superconducting magnetic energy storage (SMES) system in damping power system low-frequency electromechanical oscillations. In the paper, the SMES system is studied in the context of a single-machine infinite-bus (SMIB) power system. The mathematical model of the SMIB power system including a SMES unit is established, and the Phillips-Heffron control structure of the power system is described. Based on the principle of the complex torque coefficient (CTC) method, the expression of the complex electromagnetic torque of the entire power system including the SMES unit is derived. A nonlinear proportion-integral-differential (PID) control strategy is proposed for the SMES system to enhance the power system damping. Simulation results demonstrate that the SMES is effective in damping the power system low-frequency oscillations and the proposed nonlinear PID controller is robust to regulate the SMES unit.