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The conventional energy meters are not suitable for long operating purposes as they spend much human and material resources. Smart meters, on the other hand, are devices that perform advanced functions including electrical energy consumption recording of residential/industrial users, billing, real-time monitoring, and load balancing. In this chapter, a smart home prototype is designed and implemented. Appliances are powered by the grid during daytime, and a photovoltaic panel stored power during the night or in case of an electricity outage. Second, consumed power from both sources is sensed and further processed for cumulative energy, cost calculations and bill establishment for different proposed scenarios using LABVIEW software. Data are communicated using a USB data acquisition card (DAQ-USB 6008). Finally, a simulation framework using LABVIEW software models four houses each equipped with various appliances. The simulator predicts different power consumption profiles to seek of peak-demand reduction through a load control process.

Power quality disturbances have adverse impacts on the electric power supply as well as on the customer equipment. Therefore, the detection and classification of such problems is necessary. In this paper, a fast detection algorithm for power quality disturbances is presented. The proposed method is a hybrid of two algorithms, abc-0dq transformation and 90• phase shift algorithms. The proposed algorithm is fast and reliable in detecting most voltage disturbances in power systems such as voltage sags, voltage swells, voltage unbalance, interrupts, harmonics, etc. The three-phase utility voltages are sensed separately by each of the algorithms. These algorithms are combined to explore their individual strengths for a better performance. When a disturbance occurs, both algorithms work together to recognize this distortion. This control method can be used for critical loads protection in case of utility voltage distortion. Simulation and analysis results obtained in this study illustrate high performance of the strategy in different single-phase and three-phase voltage distortions.

In recent years, Power Quality becomes increasingly a major concern for both electric utilities and end users. Accordingly, the electrical engineering community has to deal with the analysis, diagnosis and solution of PQ issues using system approach rather than handling these issues as individual problems. This paper describes the analysis of PQ using advanced signal processing tools represented in Hilbert & Wavelet Transforms (HT-WT) and artificial intelligence tools represented in Artificial Neural Network & Support Vector Machine (ANN-SVM) for detection and classification of power quality disturbances respectively. These techniques were successfully simulated using LABVIEW software capabilities. The results of simulation indicate that the signal processing techniques are effective mechanisms to detect and classify power quality disturbances. At the end, the combination of WT as a tool of detection and features extraction with SVM as a classifier tool resulted as the best combination for PQ monitoring system.