Application of Deep Neural Networks to Distribution System State Estimation and Forecasting

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks (LSTMs) to mitigate the aforementioned challenges in power distribution systems. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn complex functional mapping between datasets with many features. CNNs and LSTMs are considered based upon their promise to perform time-series forecasting by learning the correlation of the dataset being predicted. The performance of MLPS, CNNs, and LSTMs to perform state estimation and state forecasting will be presented in terms of average root-mean square error (RMSE) and training execution time. An IEEE standard 34-bus test system is used to illustrate the proposed conventional neural network and deep learning methods and their effectiveness to perform power system state estimation and power system state forecasting.

IoT-Based Mobile Energy Storage Operation in Multi-MG Power Distribution Systems to Enhance System Resiliency

Multi-microgrids have gained interest in academics and industry in recent years. Multi-microgrid (MG) allows the integration of different distributed energy resources (DERs), including intermittent renewables and controllable local generators, and provides a more flexible, reliable, and efficient power grid. This research formulates and proposes a solution for finding optimal location and operation of mobile energy storage (MES) in multi-MG power distribution systems (PDS) with different resources during extreme events to maximize system resiliency. For this purpose, a multi-stage event-based system resiliency index is defined and the impact of the Internet of things (IoT) application in MES operation in multi-MG systems is investigated. Moreover, the demand and price uncertainty impact on multi-MG operational performance indices is presented. This research uses a popular PG & E 69-bus multi-MG power distribution network for simulation and case studies. A new hybrid PSO-TS optimization algorithm is constructed for the simulations to better understand the contributions of MES units and different DERs and IoT on the operational aspects of a multi-MG system. The results obtained from the simulations illustrate that optimal operation of MES and other energy resources, along with the corresponding energy sharing strategies, significantly improves the distribution system operational performance.

Probability-Based Customizable Modeling and Simulation of Protective Devices in Power Distribution Systems

Fused contactors and thermal magnetic circuit breakers are commonly applied protective devices in power distribution systems to protect the circuits when short-circuit faults occur. A power distribution system may contain various makes and models of protective devices, as a result, customizable simulation models for protective devices are demanded to effectively conduct system-level reliable analyses. To build the models, thermal energy-based data analysis methodologies are first applied to the protective devices’ physical properties, based on the manufacturer’s time/current data sheet. The models are further enhanced by integrating probability tools to simulate uncertainties in real-world application facts, for example, fortuity, variance, and failure rate. The customizable models are expected to aid the system-level reliability analysis, especially for the microgrid power systems.