Biblio

Multi-active bridge (MAB) converter has played an important role in the power conversion of renewable-based smart grids, electrical vehicles, and more/all electrical aircraft. However, the increase of MAB submodules greatly complicates the control architecture. In this regard, the conventional centralized control strategies, which rely on a single controller to process all the information, will be limited by the computation burden. To overcome this issue, this paper proposes a decentralized power management strategy for MAB converter. The switching frequencies of MAB submodules are adaptively regulated based on the submodule local information. Through this effort, flexible electrical power routing can be realized without communications among submodules. The proposed methodology not only relieves the computation burden of MAB control system, but also improves its modularity, flexibility, and expandability. Finally, the experiment results of a three-module MAB converter are presented for verification.

Power system intelligent terminal equipment is widely used in real-time monitoring, data acquisition, power management, power distribution and other tasks of smart grid. The power system intelligent terminal can obtain various information of users and power companies in the power grid, but there is still a lack of protection means for the connection and communication process of the terminal components. In this paper, a novel method based on improved deep belief network(IDBN) is proposed to accomplish the business-level security monitoring and attack detection of power system terminal. A non-intrusive business-level monitoring platform for power system terminals is established, which uses energy metering intelligent terminals as an example for non-intrusive data collection. Based on this platform, the I-DBN extracts the spatial and temporal attack characteristics of the external monitoring data of the system. Some fault conditions and cyber attacks of the model have been simulated to demonstrate the effectiveness of the proposed detection method and the results show excellent performance. The method and platform proposed in this paper can be extended to other services in the power industry, providing a theoretical basis and implementation method for realizing the security monitoring of power system intelligent terminals from the business level.

To be able to meet demanding application performance requirements within a tight power budget, runtime power management must track hardware activity at a very fine granularity in both space and time. This gives rise to sophisticated power management algorithms, which need the underlying system to be both highly observable (to be able to sense changes in instantaneous power demand timely) and controllable (to be able to react to changes in instantaneous power demand timely). The end goal is allocating the power budget, which itself represents a very critical shared resource, in a fair way among active tasks of execution. Fundamentally, if not carefully managed, any system-wide shared resource can give rise to covert communication. Power budget does not represent an exception, particularly as systems are becoming more and more observable and controllable. In this paper, we demonstrate how power management vulnerabilities can enable covert communication over a previously unexplored, novel class of covert channels which we will refer to as POWERT channels. We also provide a comprehensive characterization of the POWERT channel capacity under various sharing and activity scenarios. Our analysis based on experiments on representative commercial systems reveal a peak channel capacity of 121.6 bits per second (bps).

Most modern computing devices make available fine-grained control of operating frequency and voltage for power management. These interfaces, as demonstrated by recent attacks, open up a new class of software fault injection attacks that compromise security on commodity devices. CLKSCREW, a recently-published attack that stretches the frequency of devices beyond their operational limits to induce faults, is one such attack. Statically and permanently limiting frequency and voltage modulation space, i.e., guard-banding, could mitigate such attacks but it incurs large performance degradation and long testing time. Instead, in this paper, we propose a run-time technique which dynamically blacklists unsafe operating performance points using a neural-net model. The model is first trained offline in the design time and then subsequently adjusted at run-time by inspecting a selected set of features such as power management control registers, timing-error signals, and core temperature. We designed the algorithm and hardware, titled a BlackList (BL) core, which is capable of detecting and mitigating such power management-based security attack at high accuracy. The BL core incurs a reasonably small amount of overhead in power, delay, and area.

One essential functionality of a modern operating system is to accurately account for the resource usage of the underlying hardware. This is especially important for computing systems that operate on battery power, since energy management requires accurately attributing resource uses to processes. However, components such as sensors, actuators and specialized network interfaces are often used in an asynchronous fashion, and makes it difficult to conduct accurate resource accounting. For example, a process that makes a request to a sensor may not be running on the processor for the full duration of the resource usage; and current mechanisms of resource accounting fail to provide accurate accounting for such asynchronous uses. This paper proposes a new mechanism to accurately account for the asynchronous usage of resources in mobile systems. Our insight is that by accurately relating the user requests with kernel requests to device and corresponding device responses, we can accurately attribute resource use to the requesting process. Our prototype implemented in Linux demonstrates that we can account for the usage of asynchronous resources such as GPS and WiFi accurately.