Spatial‐temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in power systems

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

This paper proposes a novel online mode identification formula, which firstly applies the results from modal analysis to online mode identification and so overcomes the defect of measurement-based methods that results from them cannot be directly used to damp oscillations. In this formula, a Prony-based multi-signal processing (PMP) method is developed to estimate the mode shape from ringdown data and a mode shape matching method is firstly introduced for finding out which modes are activated in the real-time oscillation of a large-scale power system. Simulation studies based on China Southern Power Grid have been carried out to verify the correctness and applicability of the proposed formula and methods.

This paper proposes approaches to estimate the dominant swing mode sensitivity by utilizing Wide Area Measurements. The proposed methods are suitable to situations where availability of information of system model and operating condition is limited to a subsystem in which the control action resides. Modal frequency and mode shape of the dominant swing mode are estimated from a Wide Area Measurement System (WAMS) based mode identification technique. This information is used along with the subsystem model for estimation of eigenvalue sensitivity. Such a scenario is indeed faced in large interconnected power systems, where effect of a control action in the subsystem on a dominant inter-area mode needs to be estimated before actual implementation. Analytical formulations of sensitivity are derived using special modal characteristics (identified based on simplified system models) and a hybrid representation of the system, in which the subsystem is treated as a controller in a feedback loop to the rest of the system. Case studies with detailed models of two area four machine and IEEE 16 machine 68 bus system validate these formulations.

Estimating electromechanical oscillation modes from synchrophasor measurements in China Southern Power Grid

The overall mechanical behavior of the structure of a gravity dam is comprehensively reflected by modal information included in the measured vibration response. In this study, the dynamic elastic modulus of the dam body and foundation materials were inverted via an underwater explosion, and analyzed. First, the first three main frequencies and basic mode shapes of the dam were obtained based on the acceleration response induced by the explosion. Then, the support vector regression model optimized with the particle swarm optimization algorithm was used to invert the mechanical parameters. The results indicate that the objective function that considers dominant frequency and mode shape has a greater advantage in terms of anti-noise compared to when only the dominant frequency is considered. The inverted dynamic elastic modulus is about 1.5 times that of the static elastic modulus of the corresponding material, and the calculated modal values using the inversion parameters are basically consistent with the measured values. This method provides a new way of determining the dynamic parameters of gravity dams.

Measurement based algorithms for estimating low-frequency electromechanical modes serve as useful practical methods to monitor the modal properties of power system oscillations in real-time. This paper proposes a recursive adaptive stochastic subspace identification (RASSI) algorithm for online monitoring of power system modes using wide-area synchrophasor data. The proposed method gives an online estimation of mode frequency and damping ratio as well as mode shapes using multi-channel measurement data. It exploits both the accuracy of subspace identification and fast computational capability of recursive methods. An adaptive method is proposed to enable fast tracking of modal evolution under poorly damped conditions together with low estimation variance under quasi-steady-state conditions. The algorithms are tested using simulated data from Kundur two-area test power system as well as measured data from real systems.

The recognition of the transient dominant instability mode is of great significance for rapidly and accurately formulating transient emergency decisions in power systems. In response to the challenge of accurately distinguishing between angle instability and voltage instability, which are coupled in actual power grids, this paper explores the mapping relationship between simulation data and the stable state of the system, as well as the dominant instability mode. The method enables real‐time identification of the dominant instability mode, which bypasses complex physical mechanisms. Firstly, spatio‐temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. Next, a multihead attention mechanism is introduced to enhance sensitivity to important time steps in the sequence data. Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. Using the IEEE 10‐machine and 39‐node system as an example for simulation, the results validate that the proposed method exhibits significant advantages in terms of accuracy and applicability compared with other machine learning methods.

A set of blackout risk assessment indexes is established according to Electricity Accident (Incident) Investigation Procedures of China Southern Power Grid (CSPG) (pilot) and Guide on Operational Risk Evaluation of CSPG, which can calculate risk values of electricity accidents (incidents). The calculation process is discussed in detail. Based on the proposed indexes and OPA blackout model, a blackout risk assessment method for power system is put forward, with which blackout risk for all 500kV buses in Guangdong Power Grid are evaluated. Weak areas in power system are identified and their inducements are analyzed. The identification results accord with actual system long-term operation experience, which verifies the correctness of the indexes and method.

Dominant inter-area oscillation mode identification using local measurement and modal energy for large-scale power systems with high grid-tied VSCs penetration

Microgrids, which are small electric power systems, accommodate different distributed generations and energy storage system. When faults occur outside or inside a microgrid, the static switch between bulk power system and microgrid must immediately open the circuit. This paper presents a fault detection, classification, and localization method that is based on a multiresolution analysis of the discrete wavelet transform (DWT) and a Taguchi-based artificial neural network (ANN). The differences of wavelet entropies of the three-phase fault voltages, three-phase fault currents, and the wavelet entropies of the neutral fault current are inputs to the ANN, which is trained using orthogonal datasets that are obtained from Taguchi's experiments. The ANN identifies the faulty phase and the location of the fault. The proposed DWT and ANN are implemented in a Renesas RX62T microcontroller, which is verified by chip-in-the-loop simulation with a real-time digital simulator (OP5600, Opal Lab) and a 50 kVA static switch to show the applicability of the proposed method.

Expected to 2020, China Southern Power Grid (CSG) installed capacity of pumped‐storage power plant (PSPP) will reach 7,880 MW. This paper summarises the operation situation and describes the main functions of PSPP in CSG, mainly Guangzhou PSPP and Huizhou PSPP. According to the characteristic of Guangdong power system, various functions such as peak‐valley load regulation, frequency control, phase shifting, emergency reserve, and black start‐up of PSPP are studied here. At the same time, this paper points out the existing problems, such as economic operation of PSPP in CSG needs to be optimised and presents corresponding proposals for improvement.

We analyze Hopf bifurcation related oscillatory behavior occurring between clusters of an interconnected power system. This phenomenon is discussed through feasibility boundary calculations associated with inter-cluster oscillatory modes of the system's real-power-angle dynamics. We demonstrate examples of Hopf bifurcation related instabilities occurring between power flow distribution based clusters (weakly coupled power systems) and/or between any coherency based clusters which may also be geographically scattered. This work gives an explanation for the nature of sustained oscillations between coherent groups of generators formed in a large interconnected power system.

Weak once in power system is normally referred to the area which is prone to system collapse. This is due lower to maximum loadability within the area. On the other hand, compensation process may also lead to cluster which represents the optimal location for FACTS device installation. This paper presents cluster development using Evolutionary Particle Swarm Optimization (EPSO) for flexible alternating currents transmission system (FACTS) device installation. The technique has been implemented in a bulk power system. In the meantime voltage profile improvement is monitored while cost of installation of installation is conducted. Comparison with respect to traditional PSO was conducted which eventually resulted superiority in terms of voltage profile improvement loss minimization and cost of installation. Validation was conducted on the IEEE 118-Bus system.

The load stability in bulk power system may be seriously affected when a large amount of distributed generators (DG) stop simultaneously due to voltage sag. It is important to understand the mechanism of load instability, which cannot be analyzed by well-known P-V curve only. For analyzing load stability without detailed simulations, this paper presents torque-speed characteristic curves of induction motor considering power system condition and constant impedance loads. As the results of examining the curves in various conditions (model order reduction methods, load dropping out), this paper also presents some cautions for bulk power system modeling with DG. A bulk power system model whose lower voltage subsystems are reduced by an accurate model order reduction method is introduced in order to check out the problems by DG-stopping. As to the numerical simulation, a great number of DG-stopping leads to voltage instability in bulk power system, and the instability is greatly improved by DG-voltage regulation.

Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic