Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.