Electrical Engineering And Systems Science

The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this work, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a DC-DC buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise over-amplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications.

Imbalanced voltage sharing during the turn-off transient is a challenge for series-connected silicon carbide (SiC) MOSFET application. This article first discusses the influence of the gate-drain discharge deviation on the voltage imbalance ratio, and its primary causes are also presented and verified by LTspice simulation. Accordingly, a novel active gate drive, which aims to compensate the discharge difference between devices connected in series, is proposed and analyzed. By only using the original output of the driving IC, the proposed gate drive is realized by implementing an auxiliary circuit on the existing commercial gate drive. Therefore, unlike other active gate drives for balancing control, no extra isolations for power/signal are needed, and the number of the devices in series is unlimited. The auxiliary circuit includes three sub-circuits as a high-bandwidth current sink for regulating switching performance, a relative low-frequency but reliable sampling and control circuit for closed-loop control, and a trigger combining the former and the latter. The operational principle and the design guideline for each part are presented in detail. Experimental results validate the performance of the proposed gate drive and its voltage balancing control algorithm.

To ensure adequate and economic reserve for primary frequency response in the current and future power system, this paper proposes real-time frequency response reserve (FRR) requirement based on system inertia. This minimum FRR will help power system operators adjust the current frequency response requirement and accommodate more renewable generations while achieving a saving of both energy and facility costs. Most importantly, the ability to adaptively vary the FRR will provide the additional agility, resiliency, and reliability to the grid.

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in practical power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. This paper proposes an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed approach has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Extensive case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.

With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC server must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we in this paper propose NAFA, an adaptive processor frequency adjustment solution, to enable an effective plan of the server's energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server's processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in average request acceptance ratio and up to 50% reduction in average request processing time.

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.

This paper introduces a methodology to perform an ex-post allocation of locally generated electricity among the members of a renewable energy community. Such an ex-post allocation takes place in a settlement phase where the financial exchanges of the community are based on the production and consumption profiles of each member. The proposed methodology consists of an optimisation framework which (i) minimises the sum of individual electricity costs of the community members, and (ii) can enforce minimum self-sufficiency rates --proportion of electricity consumption covered by local production-- on each member, enhancing the economic gains of some of them. The latter capability aims to ensure that members receive enough incentives to participate in the renewable energy community. This framework is designed so as to provide a practical approach that is ready to use by community managers, which is compliant with current legislation on renewable energy communities. It computes a set of optimal repartition keys, which represent the percentage of total local production given to each member -- one key per metering period per member. These keys are computed based on an initial set of keys provided in the simulation, which are typically contractual i.e., agreed upon between the member and the manager the renewable energy community. This methodology is tested in a broad range of scenarios, illustrating its ability to optimise the operational costs of a renewable energy community.

Being capable of sensing and behavioral adaptation in line with their changing environments, cognitive cyber-physical systems (CCPSs) are the new form of applications in future wireless networks. With the advancement of the machine learning algorithms, the transmission scheme providing the best performance can be utilized to sustain a reliable network of CCPS agents equipped with self-decision mechanisms, where the interactions between each agent are modeled in terms of service quality, security, and cost dimensions. In this work, first, we provide network utility as a reliability metric, which is a weighted sum of the individual utility values of the CCPS agents. The individual utilities are calculated by mixing the quality of service (QoS), security, and cost dimensions with the proportions determined by the individualized user requirements. By changing the proportions, the CCPS network can be tuned for different applications of next-generation wireless networks. Then, we propose a secure transmission policy selection (STPS) mechanism that maximizes the network utility by using the Markov-decision process (MDP). In STPS, the CCPS network jointly selects the best performing physical layer security policy and the parameters of the selected secure transmission policy to adapt to the changing environmental effects. The proposed STPS is realized by reinforcement learning (RL), considering its real-time decision mechanism where agents can decide automatically the best utility providing policy in an altering environment.

After major disturbances, power system behavior is governed by the dynamic characteristics of its assets and protection schemes. Therefore, modeling protection devices is essential for performing accurate stability studies. Modeling all the protection devices in a bulk power system is an intractable task due to the limitations of current stability software, and the difficulty of maintaining and updating the data for thousands of protection devices. One of the critical protection schemes that is not adequately modeled in stability studies is distance relaying. Therefore, this paper proposes an algorithm that uses two methods to identify the critical distance relays to be modeled in stability studies: (i) apparent impedance monitoring, and (ii) the minimum voltage evaluation (MVE). The algorithm is implemented in Python 3.6 and uses the GE positive sequence load flow analysis (PSLF) software for performing stability studies. The performance of the algorithm is evaluated on the Western Electricity Coordinating Council (WECC) system data representing the 2018 summer-peak load. The results of the case studies representing various types of contingencies show that to have an accurate assessment of system behavior, modeling the critical distance relays identified by the algorithm suffices, and there is no need for modeling all the distance relays.