Novel adaptive stability enhancement strategy for power systems based on deep reinforcement learning

As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

A Machine Learning Approach for Detecting Unemployment using the Smart Metering Infrastructure

Technological advancements in the field of electrical energy distribution and utilization are revolutionizing the way consumers and utility providers interact. In addition to allowing utility companies to monitor the status of their network independently in autonomous fashion, data collected by smart meters as part of the wider advanced metering infrastructure, can be valuable for third parties, such as government authorities. The availability of the information, the granularity of the data, and the real-time nature of the smart meter, means that predictive analytics can be employed to profile consumers with high accuracy and approximate, for example, the number of individuals living in a house, the type of appliances being used, or the duration of occupancy, to name but a few applications. This paper presents a machine learning model comparison for unemployment prediction of single household occupants, based on features extracted from smart meter electricity readings. A number of nonlinear classifiers are compared, and benchmarked against a generalized linear model, and the results presented. To ensure the robustness of the classifiers, we use repeated cross validation. The results revealed that it is possible to predict employability status with Area Under Curve (AUC) = 74%, Sensitivity (SE) = 54% and Specificity (SP) = 83%, using a multilayer perceptron neural network with dropout, closely followed by the results produced by a distance weighted discrimination with polynomial kernel model. This shows the potential of using the smart metering infrastructure to provide additional autonomous services, such as unemployment detection, for governments using data collected from an advanced and distributed Internet of Things (IoT) sensor network

Design criteria of a transcutaneous power delivery system for implantable devices.

Implantable cardiac assist devices such as artificial hearts and blood pumps are a rapidly growing therapy used for treating moderate to severe congestive heart failure. While current treatments offer improved heart failure survival and increased patient functionality with enhanced quality of life, powering these devices are still constraining. In practice, percutaneous cables passing through skin are used for power and control data transmission requiring patients to maintain a sterile dressing on the skin cable-exit site. This contact site limits patient movement as it is vulnerable to wound infection due to trauma and poor healing. As a result, a sterile dressing has to be maintained and nursed regularly for treating the wound. Complications from the exit site infections are a leading cause of death in long-term support with these devices. Wireless power and control transmission systems have been studied and developed over years in order to avoid percutaneous cables while supplying power efficiently to the implanted device. These power systems, commonly named Transcutaneous Energy Transfer (TET) systems, enable power transmission across the skin without direct electrical connectivity to the power source. TET systems use time-varying electromagnetic induction produced by a primary coil that is usually placed near skin outside the body. The induced voltage in an implanted secondary coil is then rectified and regulated to transfer energy to an implanted rechargeable battery in order to power the biomedical load device. Efficient and optimum energy transfer using such transcutaneous methods is more complex for mobile patients due to coupling discrepancies caused by variations in the alignment of the coil. The research studies equivalent maximum power transfer topologies for evaluating voltage gain and coupling link efficiency of TET system. Also, this research adds to previous efforts by generalizing different scenarios of misalignments of different coil size that affects the coupling link. As a whole, this study of geometric coil misalignments reconsiders potential anatomic location for coil placement to optimize TET systems performance in anticipated environment for efficient and safe operation.--Abstract

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio