Non-intrusive load monitoring is an algorithm or process that disaggregates the total power in a facility to identify consumption of individual appliances. In this paper, a new algorithm is proposed to classify events of appliance states based on modification of the Cross-Entropy method. The main contribution is a formulation and solution of the problem with the Cross-Entropy method as a constrained optimization problem. This new technique is called the Modified Cross-Entropy (MCE) method. The proposed algorithm is simple, as it operates in real time, using low rate sampling of the active power. In addition, there is no need to train the system, or to use complex hardware. The algorithm is tested using the REDD and AMPds datasets, and the results are compared to several state-of-the-art techniques.

This paper addresses the challenge of fault location in large power networks using a limited number of sensors. It was recently shown that power system faults may be modeled by sparse vectors, and hence, can be located efficiently using sparse recovery techniques. In this paper, we extend this approach and propose a sparse recovery algorithm that exploits both the sparsity constraints and additional structural constraints imposed by the faults physical models. To this end, faults are represented by sparse vectors that are subjected to nonconvex constraints. These constraints are shown to provide additional information that is exploited to reduce the number of measurements and to improve the location accuracy. The algorithm searches directly over these physical faults, and therefore, operates over a small solution space. Simulations on the IEEE 118 bus test-case network show that 4 to 20 sensors are sufficient to recover faults with adequate accuracy. With 20 PMU sensors, more than 99% of single-fault events and 84%-95% of two-fault events are located, depending on fault types and SNR.

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitor banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, a general formulation of the multi-objective Volt-VAr Optimization problem is proposed. The applicability of various multi-optimization techniques is considered and the operational interpretation of these solutions is discussed. The methods are demonstrated using a simulation on a test feeder.