The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.

Multiobjective evolutionary algorithms for electric power dispatch problem

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Analysis of Dynamic Behaviour of Electric Power Systems with Large Amount of Wind Power

This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.

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Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

A power system stabilizer (PSS) for a wind turbine employing a doubly fed induction generator (DFIG) is presented. It is shown that this PSS can significantly influence the contribution that a DFIG-based wind farm can make to network damping. A simple, generic test network that combines synchronous and wind farm generation is used to demonstrate system performance contributions. The results of both eigenvalue analysis and time response simulation studies are presented to illustrate contributions to network dynamic and transient performance that the DFIG controller with its PSS can make. Performance capabilities superior to those provided by synchronous generation with automatic voltage regulator and PSS control are demonstrated.

A power system stabilizer for DFIG-based wind generation

Alarm systems play critically important roles for the safe and efficient operation of modern industrial plants. However, most existing industrial alarm systems suffer from poor performance, noticeably having too many alarms to be handled by operators in control rooms. Such alarm overloading is extremely detrimental to the important role played by alarm systems. This paper provides an overview of industrial alarm systems. Four main causes are identified as the culprits for alarm overloading, namely, chattering alarms due to noise and disturbance, alarm variables incorrectly configured, alarm design isolated from related variables, and abnormality propagation owing to physical connections. Industrial examples from a large-scale thermal power plant are provided as supportive evidences. The current research status for industrial alarm systems is summarized by focusing on existing studies related to these main causes. Eight fundamental research problems to be solved are formulated for the complete lifecycle of alarm variables including alarm configuration, alarm design, and alarm removal.

An Overview of Industrial Alarm Systems: Main Causes for Alarm Overloading, Research Status, and Open Problems

A global multiscale method based on a dynamic mode decomposition (DMD) algorithm to characterize the global behavior of transient processes recorded using wide-area sensors is proposed. The method interprets global dynamic behavior in terms of both, spatial patterns or shapes and temporal patterns associated with dynamic modes containing essentially single-frequency components, from which the mode shapes, frequencies and growth and decay rates of the modes can be extracted simultaneously. These modes are then used to detect the coherent and dominant structures within the data. The technique is well suited for fast wide-area monitoring and assessment of global instability in the context of modern data fusion-based estimation techniques. Results of the application of the proposed method to large, high-dimensional data sets are encouraging.

A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis

Identification of Electromechanical Modes in Power Systems

This study provides a review of methods used for stability monitoring in two different fields, electrical power systems and vibration analysis, with the aim of increasing awareness of and highlighting opportunities for cross-fertilisation. The nature of the problems that require stability monitoring in both fields are discussed here as well as the approaches that have been taken. The review of power systems methods is presented in two parts: methods for ambient or normal operation and methods for transient or post-fault operation. Similarly, the review of methods for vibration analysis is presented in two parts: methods for stationary or linear time-invariant data and methods for non-stationary or non-linear time-variant data. Some observations and comments are made regarding methods that have already been applied in both fields including recommendations for the use of different sets of algorithms that have not been utilised to date. Additionally, methods that have been applied to vibration analysis and have potential for power systems stability monitoring are discussed and recommended.

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Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

https://vbn.aau.dk/files/284854635/paper_Gibran.pdf

Power Flow Modeling of Islanded AC Microgrids with Hierarchical Control

Linear analysis techniques are analysis tools that provide valuable engineering insights into the dynamic characteristics of a power system that are often not easily obtainable by other means of analysis. Their importance is underlined by frequent new developments and applications. One of the objectives of this panel session is to highlight the practical value of linear analysis methods as a means to acquiring a more complete understanding of power system dynamic phenomena.

Emilio Barocio

Papers

A Dynamic Mode Decomposition Framework for Global Power System Oscillation Analysis

Identification of Electromechanical Modes in Power Systems

Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

Power Flow Modeling of Islanded AC Microgrids with Hierarchical Control

Recent applications of linear analysis techniques