Electrical Engineering And Systems Science

This paper mainly introduces an infrared optical communication system based on stable and handstand pendulum. This system adopts the method of loading the infrared light emitting end on an handstand pendulum to realize the stability and controllability of the infrared light transmission light path. In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission. Experimental results show that the system can effectively ensure the stability of the transmission optical path and is suitable for accurate and stable signal transmission in bumpy environments.

Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for predicting remaining life distribution of each component, with ii) mixed integer optimization models and solution algorithms used for identifying optimal wind farm maintenance and operations is proposed. Maintenance decisions identify optimal times to repair every component, which in turn, determine the failure risk of the turbines. More specifically, optimization models that characterize a turbine's failure time as the first time that one of its constituent components fail - a systems reliability concept called competing risk is developed. The resulting turbine failures impact the optimization of wind farm operations and revenue. Extensive experiments conducted for multiple wind farms with 300 wind turbines - 1200 components - showcases the performance of the proposed framework over conventional methods.

Within the past two decades, Gaussian process regression has been increasingly used for modeling dynamical systems due to some beneficial properties such as the bias variance trade-off and the strong connection to Bayesian mathematics. As data-driven method, a Gaussian process is a powerful tool for nonlinear function regression without the need of much prior knowledge. In contrast to most of the other techniques, Gaussian Process modeling provides not only a mean prediction but also a measure for the model fidelity. In this article, we give an introduction to Gaussian processes and its usage in regression tasks of dynamical systems. Try Gaussian process regression yourself: this https URL

The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which are stationary points but may not be globally optimal. In this paper, we propose a simple iterative approach to find globally optimal solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We show the effectiveness our algorithm on standard IEEE networks. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations.

This paper proposes a novel online measurement-based Wide-Area Voltage Control (WAVC) method using Phasor Measurement Unit (PMU) data in power systems with Flexible AC Transmission System (FACTS) devices. As opposed to previous WAVC methods, the proposed WAVC does not require any model knowledge or the participation of all buses and considers both active and reactive power perturbations. Specifically, the proposed WAVC method exploits the regression theorem of the Ornstein-Uhlenbeck process to estimate the sensitivity matrices through PMU data online, which are further used to design and apply the voltage regulation by updating the reference points of FACTS devices. Numerical results on the IEEE 39- Bus and IEEE 68-Bus systems demonstrate that the proposed model-free WAVC can provide effective voltage control in various network topologies, different combinations of voltage-controlled and voltage-uncontrolled buses, under measurement noise, and in case of missing PMUs. Particularly, the proposed WAVC algorithm may outperform the model-based WAVC when an undetected topology change happens.

An open source software package for performing dynamic RMS simulation of small to medium-sized power systems is presented, written entirely in the Python programming language. The main objective is to facilitate fast prototyping of new wide area monitoring, control and protection applications for the future power system by enabling seamless integration with other tools available for Python in the open source community, e.g. for signal processing, artificial intelligence, communication protocols etc. The focus is thus transparency and expandability rather than computational efficiency and performance. The main purpose of this paper, besides presenting the code and some results, is to share interesting experiences with the power system community, and thus stimulate wider use and further development. Two interesting conclusions at the current stage of development are as follows: First, the simulation code is fast enough to emulate real-time simulation for small and medium-size grids with a time step of 5 ms, and allows for interactive feedback from the user during the simulation. Second, the simulation code can be uploaded to an online Python interpreter, edited, run and shared with anyone with a compatible internet browser. Based on this, we believe that the presented simulation code could be a valuable tool, both for researchers in early stages of prototyping real-time applications, and in the educational setting, for students developing intuition for concepts and phenomena through real-time interaction with a running power system model.

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used Upper Confidence Bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation trade-off when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root.

An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC methods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture - periodic model and disturbance update and convexification of the planning problem - are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.

Efficient residential sector coupling plays a key role in supporting the energy transition. In this study, we analyze the structural properties associated with the optimal control of a home energy management system and the effects of common technological configurations and objectives. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic (PV) system, a battery, and thermal storage systems for floor heating and hot-water supply. In addition, we allow grid feed-in by assuming fixed feed-in tariffs and consider user comfort. In our numerical analysis, we find that the battery, naturally, is the essential building block for improving self-sufficiency. However, in order to use the PV surplus efficiently grid feed-in is necessary. The commonly considered objective of maximizing self-consumption is not economically viable under the given tariff structure; however, close-to-optimal performance and significant reduction in solution times can be achieved by maximizing self-sufficiency. Based on optimal control and considering seasonal effects, the dominant order of PV distribution and the target states of charge of the storage systems can be derived. Using a rolling horizon approach, the solution time can be reduced to less than 1 min (achieving a time resolution of 1 h per year). By evaluating the value of information, we find that the common value of 24 h for the prediction and control horizons results in unintended but avoidable end-of-horizon effects. Our input data and mixed-integer linear model developed using the Julia JuMP programming language are available in an open-source manner.

We illustrate that the electrification of our transport system might impose unnecessary extra congestion and delay for daily commuting passengers. By modelling travel behaviors of these passengers, it is found that more of them tend to depart at a narrower peak-hour time window. The occurrence of this shift is mainly caused by (1) the energy consumption of electric vehicles (EVs) is much lower than that of traditional vehicles and (2) the energy consumption of EVs is less sensitive to congestion than that of traditional vehicles. We further examine the role of congestion toll in minimizing the extra congestion and delay.