: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

Nonlinear Time Series Analysis.

(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

ライブラリー Annual Energy Outlook 2000

Elementary Fixed Point Theorems * Theorem of Borsuk and Topological Transversality * Homology and Fixed Points * Leray-Schauder Degree and Fixed Point Index * The Lefschetz-Hopf Theory * Selected Topics * Index

Fixed Point Theory

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Stochastic Processes And Filtering Theory

Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced and implemented. Various Kalman filters and the observer are then tested on the 16-machine, 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for Kalman filter routines and observers.

Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks

For renewable energy penetration, a frequency control strategy is proposed to assure the multi-area hybrid power system stable operation. The load frequency control (LFC) and the battery energy storage system (BESS) are used together to design the frequency control strategy. Furthermore, in order to improve the control accuracy and reduce the battery capacity, the area control error and active power disturbance are divided into low-frequency components and high-frequency components. Therefore, LFC is designed by using sliding mode algorithm for the low-frequency deviation and BESS is adjusted by using the designed disturbance observer for the high-frequency disturbance. Finally, the effectiveness of the proposed frequency control strategy is proved by the real-time digital simulator which can optimise the frequency quality and reduces the capacity of the BESS.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2018.5145

Frequency control strategy of multi-area hybrid power system based on frequency division and sliding mode algorithm

Battery swapping stations (BSSs) have great potential in providing fast frequency regulation service (FFRS) owing to their large battery storage capacity. However, compared to a regular BSS, a BSS providing FFRS faces the following financial risks: first, higher infrastructure investment to support the vehicle-to-grid services; second, higher battery aging costs due to FFRS; third, FFRS causes uncertainties to batteries’ charging costs. Under such a context, in this article, we propose an economic risk assessment model for the BSS-based FFRS by comparing its economics with a regular BSS. In this model, the value at risk (VaR) of daily revenue and the long-term return on investment (ROI) of a regular BSS and a BSS providing FFRS are compared. The assessment results are obtained in three steps: first, we develop the operation and economic models for the BSS-based FFRS. Next, the mathematical models of the VAR and ROI analyses are formulated. Finally, the VaR of daily revenue is compared through a statistical analysis of a large number of scenarios. The ROI comparison is conducted by a policy gradient based reinforcement learning algorithm, which can handle the nonconvexity and stochastic dynamics brought by the electric vehicle visits. The practicality of the proposed framework is demonstrated by using the real ancillary market data from utilities and the traffic count data from onsite traffic sensors.

Economic Assessment for Battery Swapping Station Based Frequency Regulation Service

With the fast growth of wind power penetration, power systems need additional flexibility to cope with wind power ramping. Several electricity markets have established requirements for flexible ramping capacity (FRC) reserves. This paper addresses two crucial issues that have rarely been discussed in the literature: 1) how to characterize wind power ramping under different forecast values and 2) how to achieve a reasonable trade-off between operational risks and FRC costs. Regarding the first issue, this paper proposes a concept of conditional distributions of wind power ramping, which is empirically verified by using simulation and real-world data. For the second issue, this paper develops an adjustable chance-constrained approach to optimally allocate FRC reserves. Equivalent tractable forms of the original problem are devised to improve computational efficiency. Tests carried out on a modified IEEE 118-bus system demonstrate the effectiveness and efficiency of the proposed method.

An Adjustable Chance-Constrained Approach for Flexible Ramping Capacity Allocation

The conductor temperature is a critical state variable that should be considered in power flow calculations to improve the calculation accuracy and reveal the potential thermal loadability of transmission components. This paper proposes a calculation method for time-process power flow considering the thermal behavior (TPPFCTB) of transmission components to calculate the temperature dynamics of overhead lines, power cables, and transformers under anticipated system operation scenarios. The TPPFCTB model is represented by differential-algebraic equations, which can be solved as a sequence of extended power flow problems at discretization time intervals. Based on the Newton method, incomplete and complete decoupling methods are proposed to decouple the correction equations into several independent groups with constant coefficient matrices, thus improving computational efficiency. The work presented in this paper provides an effective method for the detection of potential power transfer capability problems associated with transmission components, thus guiding operators to make more economical decisions. Numerical simulations were conducted to verify the effectiveness of the proposed method.

Jianhui Wang

Papers

Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks

Frequency control strategy of multi-area hybrid power system based on frequency division and sliding mode algorithm

Economic Assessment for Battery Swapping Station Based Frequency Regulation Service

An Adjustable Chance-Constrained Approach for Flexible Ramping Capacity Allocation

Time-Process Power Flow Calculation Considering Thermal Behavior of Transmission Components