Decentralized Secondary Control Scheme for Frequency Restoration in Inverter-based Islanded Microgrids

Inverter-dominated microgrids are quickly becoming a key building block of future power systems. They rely on centralized controllers that can provide reliability and resiliency in extreme events. Nonetheless, communication failures due to cyber-physical attacks or natural disasters can make autonomous operation of islanded microgrids challenging. This paper examines a unified decentralized secondary control scheme that is robust to inverter clock synchronization errors and can be seamlessly applied to grid-following or grid-forming control architectures. The proposed scheme overcomes the well-known stability problem that arises from parallel operation of local integral controllers. Theoretical guarantees for stability are provided along with criteria to appropriately tune the secondary control gains to achieve good frequency regulation performance while ensuring fair power sharing. The efficacy of our approach in eliminating the steady-state frequency deviation is demonstrated through simulations on a 5-bus microgrid with four grid-forming inverters.Comment: 7 pages, 9 figure

Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems

Typical WLAN based indoor positioning systems take the received signal strength (RSS) as the major information source. Due to the complicated indoor environment, the RSS measurements are hard to model and too noisy to achieve a satisfactory 3-D accuracy in multi-floor scenarios. To enhance the performance of WLAN positioning systems, extra information sources could be integrated. In this paper, a Bayesian framework is applied to fuse multi-information sources and estimate the spatial and time varying parameters simultaneously and adaptively. An application of this framework, which fuses pressure measurements, a topological building map with RSS measurements, and simultaneously estimates the pressure sensor bias, is investigated. Our experiments indicate that the localization performance is more accurate and robust by using our approach

WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Indoor positioning systems based on Wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework

Enhancing the map usage for indoor location-aware systems

Location-aware systems are receiving more and more interest in both academia and industry due to their promising prospective in a broad category of so-called Location-Based-Services (LBS). The map interface plays a crucial role in the location-aware systems, especially for indoor scenarios. This paper addresses the usage of map information in a Wireless LAN (WLAN)-based indoor navigation system. We describe the benefit of using maNMp information in multiple algorithms of the system, including radio-map generation, tracking, semantic positioning and navigation. Then we discuss how to represent or model the indoor map to fulfill the requirements of intelligent algorithms. We believe that a vector-based multi-layer representation is the best choice for indoor location-aware system

Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning

Indoor WLAN positioning should be modeled as a nonlinear and non-Gaussian dynamic system due to the complex indoor environment, radio propagation and motion behaviour. The aim of this paper is to analyze different filtering strategies for real life indoor WLAN positioning systems. The performance criteria for the comparison are the mean of localization errors and computational complexity. Three nonlinear filters are analyzed: Fourier density approximation (FF), particle filter (PF) and grid-based filter (GF), which are representatives for deterministic and random density approximation approaches. Our experimental results help to choose the appropriate filtering techniques under different resource limitations

Nonlinear phase FIR filter design with minimum LS error and additional constraints

Journal PaperWe examine the problem of approximating a complex frequency response by a real-valued FIR filter according to the <i>L₂</i> norm subject to additional inequality constraints for the complex error function. Starting with the Kuhn-Tucker optimality conditions which specialize to a system of nonlinear equations we deduce an iterative algorithm. These equations are solved by Newton's method in every iteration step. The algorithm allows arbitrary tradeoffs between an <i>L₂</i> and an <i>L_oo</i> design. The <i>L₂</i> and the <i>L_oo</i> solution result as special cases

Enhancing the Map Usage for Indoor Location-Aware Systems

Abstract. Location-aware systems are receiving more and more interest in both academia and industry due to their promising prospective in a broad category of so-called Location-Based-Services (LBS). The map interface plays a crucial role in the location-aware systems, especially for indoor scenarios. This paper addresses the usage of map information in a Wireless LAN (WLAN)-based indoor navigation system. We describe the benefit of using map information in multiple algorithms of the system, including radio-map generation, tracking, semantic positioning and navigation. Then we discuss how to represent or model the indoor map to fulfill the requirements of intelligent algorithms. We believe that a vector-based multi-layer representation is the best choice for indoor location-aware system. Keyword: Location-Aware Systems, WLAN Positioning, Map Representation

Fusion of Barometric Sensors, WLAN Signals and Building Information for 3-D Indoor/Campus Localization

Abstract—Location estimation in indoor/campus environments has attracted much interest for its broad applications. Many applications (e.g. personnel security) require not only the 2-D coordinate but also the floor index where the mobile users are situated. However, most of the current location systems cannot provide the floor information accurately and robustly. In this paper, we propose a 3-D localization scheme which fuses the barometric sensor with Wireless LAN (WLAN) signals and building information. Our experiments show that this fusion scheme can both identify the floor index without errors and improve the horizontal localization accuracy. Moreover, since the barometric sensor is quite simple and cheap, it would bring almost no increase in system costs