(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

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Voltage Stability Of Electric Power Systems

We propose an online framework to detect cyberattacks on automatic generation control. A cyberattack detection algorithm is designed based on the approach of dynamic watermarking. The detection algorithm provides a theoretical guarantee of detection of cyberattacks launched by sophisticated attackers possessing extensive knowledge of the physical and statistical models of targeted power systems. The proposed framework is practically implementable, as it needs no hardware update on generation units. The efficacy of the proposed framework is validated in both four-area system and 140-bus system.

An Online Detection Framework for Cyber Attacks on Automatic Generation Control

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate P – V curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximant method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P – V curve with moderate computational burden. The proposed method is first illustrated in detail on a four-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council 48-geneartor 140-bus power system.

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

Load characteristics have substantial influence on the voltage stability of power systems. The self-restorative characteristic and stalling of induction motor loads can deteriorate the voltage stability, so it is necessary to develop accurate and efficient dynamic analysis methods for voltage stability analysis of systems with induction motor loads. In this paper, a set of methods based on holomorphic embedding is proposed, which is able to solve steady states and dynamics of a power system with induction motors. The paper is the first work that applies holomorphic embedding to dynamic problems of power systems. The test cases on the IEEE 14-bus system, the NPCC 140-bus system, and the Polish 2383-bus system verify both accuracy and efficiency of the proposed methods.

Voltage Stability Analysis of Power Systems With Induction Motors Based on Holomorphic Embedding

The recently proposed non-iterative load flow method, called the holomorphic embedding method, may encounter the precision issue, i.e., the nontrivial round-off errors caused by the limited digits used in computing the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New England 39-bus power system and benchmarked with the result from the traditional continuation power flow method.

Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve

This paper proposes an online steady-state voltage stability assessment scheme to evaluate the proximity to voltage collapse at each bus of a load area. Using a non-iterative holomorphic embedding method (HEM) with a proposed physical germ solution, an accurate loading limit at each load bus can be calculated based on online state estimation on the entire load area and a measurement-based equivalent for the external system. The HEM employs a power series to calculate an accurate Power-Voltage (P-V) curve at each load bus and accordingly evaluates the voltage stability margin considering load variations in the next period. An adaptive two-stage Pade approximants method is proposed to improve the convergence of the power series for accurate determination of the nose point on the P-V curve with moderate computational burden. The proposed method is illustrated in detail on a 4-bus test system and then demonstrated on a load area of the Northeast Power Coordinating Council (NPCC) 48-geneartor, 140-bus power system.

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.

Image Embedding of PMU Data for Deep Learning Towards Transient Disturbance Classification

The recently proposed non-iterative load flow method, called the holomorphic embedding method, may encounter the precision issue, i.e. the nontrivial round-off errors caused by the limited digits used in computing the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New England 39-bus power system and benchmarked with the result from the traditional continuation power flow method.

Chengxi Liu

Papers

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve

Online Voltage Stability Assessment for Load Areas Based on the Holomorphic Embedding Method

Image Embedding of PMU Data for Deep Learning Towards Transient Disturbance Classification

Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve