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Multivariate Data Analysis With Readings

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable At last, a typical deep learning, ie, stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks

Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks

Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis

The intention of this paper is to provide an overview of using agent and service-oriented technologies in intelligent energy systems. It focuses mainly on ongoing research and development activities related to smart grids. Key challenges as a result of the massive deployment of distributed energy resources are discussed, such as aggregation, supply-demand balancing, electricity markets, as well as fault handling and diagnostics. Concepts and technologies like multiagent systems or service-oriented architectures are able to deal with future requirements supporting a flexible, intelligent, and active power grid management. This work monitors major achievements in the field and provides a brief overview of large-scale smart grid projects using agent and service-oriented principles. In addition, future trends in the digitalization of power grids are discussed covering the deployment of resource constrained devices and appropriate communication protocols. The employment of ontologies ensuring semantic interoperability as well as the improvement of security issues related to smart grids is also discussed.

/pdf/a-review-of-agent-and-service-oriented-concepts-applied-to-5fp6wow9r7.pdf

A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems

State estimation and control approaches in electric distribution grids rely on precise electric models that may be inaccurate. This work presents a novel method of estimating distribution line parameters using only root mean square voltage and power measurements under consideration of measurement tolerances, noise, and asynchronous timestamps. A measurement tolerance compensation model and an alternative representation of the power flow equations without voltage phase angles are introduced. The line parameters are obtained using numeric methods. The simulation demonstrates in case of the series conductance that the absolute compensated error is $-1.05\%$ and $-1.07\%$ for both representations, as opposed to the expected uncompensated error of $-79.68\%$ . Identification of a laboratory distribution line using real measurement data grid yields a deviation of 6.75% and 4.00%, respectively, from a calculation based on the manufacturer’s cable specifications and estimated line length. The transformed power flow equations deliver similar results despite the reduced problem complexity.

Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances

https://backend.orbit.dtu.dk/ws/files/133384487/Comparison_between_Synthetic_Inertia_and_Fast_Frequency_Containment_Control_Based_on_Single_Phase_EVs_in_a_Microgrid.pdf

Comparison between Synthetic Inertia and Fast Frequency Containment Control Based on Single Phase EVs in a Microgrid

https://backend.orbit.dtu.dk/ws/files/216373347/BornholmStabilityPaper_rev.pdf

Large-scale provision of frequency control via V2G: The Bornholm power system case

Low-carbon and energy efficient technologies are seen as key enablers to reduce the green-house gas emissions and to limit the global warming The large scale penetration of distributed energy resources from renewables seems to be a very promising approach In order to cope with the fluctuating nature of such energy resources an intelligent integration in today's electric energy infrastructure is necessary Such intelligent power networks--called Smart Grids--tend to have a higher complexity compared to the traditional infrastructure They need advanced control approaches in order to be manageable The development of more sophisticated information, communication and automation technologies and control algorithms are in the focus of the research community today in order to master the higher complexity of Smart Grid systems This paper provides a review of automation trends and challenges for the future electric energy infrastructure with focus on advanced concepts using artificial intelligence and multi-agent systems Moreover, most important standards and common rules are also discussed in order to satisfy interoperability issues in such a distributed environment

Review of Trends and Challenges in Smart Grids: An Automation Point of View

The increasing share of volatile and inverter-based energy sources render electric power grids increasingly susceptible to disturbances. Established Load Frequency Controls (LFC) schemes are rigid and require careful tuning, making them unsuitable for dynamically changing environments. In this paper, we present a fast and tuningless frequency control approach that tackles these shortcomings by means of modern grid monitoring and communications infrastructures in a twofold concurrent process. First, direct observation of supply and demand enables fast power balancing decoupled from the total system dynamics. Second, primary resources are actively involved in frequency restoration by systematic adjustment of their frequency reference setpoints. In contrast to the commonly used Automatic Generation Control (AGC), the proposed direct LFC does not require an integrator for frequency control in the closed loop even under partial grid observability. The approach is Lyapunov-stable for a wide range of system parameters, including ramping limits of controlled resources. A performance study against AGC has been conducted on a three-area power system in simulations as well as in a real-laboratory grid with an installed generation capacity of 110 kW.

/pdf/tuningless-load-frequency-control-through-active-engagement-5e5wiwmtqa.pdf

Alexander Prostejovsky

Papers

Distribution Line Parameter Estimation Under Consideration of Measurement Tolerances

Comparison between Synthetic Inertia and Fast Frequency Containment Control Based on Single Phase EVs in a Microgrid

Large-scale provision of frequency control via V2G: The Bornholm power system case

Review of Trends and Challenges in Smart Grids: An Automation Point of View

Tuningless Load Frequency Control Through Active Engagement of Distributed Resources