The ever growing penetration of distributed generation (DG) in a distribution network has a profound impact on network protection and stability. Traditional protection schemes and algorithms need to be extensively investigated as more and more DGs get introduced into the network. The current version of IEEE Standard 1547 does not present a comprehensive solution for fault current detection in the presence of various kinds of DGs. Power electronic inverter-based DGs (IBDGs) are of special concern in distribution network protection as they are often incapable of providing sufficient fault current and their controllers play a principal role in the DG behavior. In this paper, the effects of voltage and current controllers for IBDGs on industrial and commercial power system protection schemes are investigated. It is shown that the inverter control mode has a direct impact on its fault current levels and duration. A simplified distribution network model with IBDG operating under voltage and current control modes was tested to verify the effects of these controllers. This paper also proposes an adaptive relaying algorithm to detect the faults in the presence of IBDGs with various types of controllers.

This paper develops analytical principles for torque ripple reduction in interior permanent magnet (IPM) synchronous machines. The significance of slot harmonics and the benefits of stators with odd number of slots per pole pair are highlighted. Based on these valuable analytical insights, this paper proposes coordination of the selection of stators with odd number of slots per pole pair and IPM rotors with multiple layers of flux barriers in order to reduce torque ripple. The effectiveness of using stators with odd number of slots per pole pair in reducing torque ripple is validated by applying a finite-element-based Monte Carlo optimization method to four IPM machine topologies, which are combinations of two stator topologies (even or odd number of slots per pole pair) and two IPM rotor topologies (one- or two-layer). It is demonstrated that the torque ripple can be reduced to less than 5% by selecting a stator with an odd number of slots per pole pair and the IPM rotor with optimized barrier configurations, without using stator/rotor skewing or rotor pole shaping.

This paper investigates the effect of uncertainty in the allocation of photovoltaic (PV) generation, solar irradiance, and its impact on the power flow in a distribution network. The solar irradiance available in the National Renewable Energy Laboratory (NREL) Resource Data Center is clustered into two states: high and low irradiance defined by a threshold. The uncertainty is modeled based on Non-Gaussian distribution, obtained using kernel density estimation. This estimation aids in achieving the probability density function and cumulative distribution functions of the solar irradiance. Moreover, the load demand, wind speed, and generator location are modeled according to Gaussian, Weibull, and discrete uniform distribution functions, respectively. As part of probabilistic power flow, the backward/forward sweep method is used to solve each scenario of the Monte Carlo Simulation. The proposed framework is applied to the 33-node test system considering three different test cases. The first case considers deployment of PV systems in three microgrids of the electric grid, and the other two test cases analyze different levels of penetration of randomly allocated PV and wind power systems. At the end, the results indicate potential reverse power flow through certain branches of the grid, and the renewables have a major impact on the system.

Microgrids with distributed energy resources are being favored in various communities to lower the dependence on utility-supplied energy and cut the CO₂ emissions from coal-based power plants. This paper presents a co-optimization strategy for distributed energy resource planning to minimize total annualized cost at the maximal fuel savings. Furthermore, the proposed scheme aids the community microgrids in satisfying the requirements of U.S. Department of Energy (DOE) and state renewable energy mandates. The method of Lagrange multipliers is employed to maximize fuel savings by satisfying Karush-Kuhn-Tucker conditions. With the Fourier transform and particle swarm optimization, the right mix of distributed energy resources is determined to decrease the annualized cost. A case study to test the proposed scheme for a community microgrid is presented. To validate its effectiveness, an economic justification of the solution and its comparison with HOMER Pro are also illustrated.

This paper presents a novel modified Viterbi algorithm to identify the optimal distribution system restoration plan for improving the grid resiliency. In the proposed algorithm, the switching operations performed for system restoration are the states with the minimum bus voltage being seen as the cost metric for each state and the extent of load recovery as the observed event. When full load recovery is spotted, the dynamic programming algorithm stops, thereby giving the least number of switching pairs necessary for system restoration. Moreover, an improved flexible switching pair operation is employed to maintain the radial nature of distribution system. Several case studies are presented for verifying the performance of the proposed strategy. Multi-fault conditions are considered in testing the system restoration scheme on 33-bus and 69-bus distribution systems. Furthermore, the effects of integrating distributed energy resources and microgrid systems are analyzed.