Mohsen Mahoor

Alireza Majzoobi, Mohsen Mahoor, Amin Khodaei

Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.

Mohsen Mahoor, Alireza Majzoobi, Amin Khodaei

Distribution Asset Management is an important task performed by utility companies to prolong the lifetime of the critical distribution assets and to accordingly ensure grid reliability by preventing unplanned outages. This study focuses on microgrid applications for distribution asset management as a viable and less expensive alternative to traditional utility practices in this area. A microgrid is as an emerging distribution technology that encompasses a variety of distribution technologies including distributed generation, demand response, and energy storage. Moreover, the substation transformer, as the most critical component in a distribution grid, is selected as the component of the choice for asset management studies. The resulting model is a microgrid-based distribution transformer asset management model in which microgrid exchanged power with the utility grid is reshaped in such a way that the distribution transformer lifetime is maximised. Numerical simulations on a test utility-owned microgrid demonstrate the effectiveness of the proposed model to reshape the loading of the distribution transformer at the point of interconnection in order to increase its lifetime.

Alireza Majzoobi, Mohsen Mahoor, Amin Khodaei

The growing proliferation of microgrids and distributed energy resources in distribution networks has resulted in the development of Distribution Market Operator (DMO). This new entity will facilitate the management of the distributed resources and their interactions with upstream network and the wholesale market. At the same time, DMOs can tap into the flexibility potential of these distributed resources to address many of the challenges that system operators are facing. This paper investigates this opportunity and develops a distribution market scheduling model based on upstream network ramping flexibility requirements. That is, the distribution network will play the role of a flexibility resource in the system, with a relatively large size and potential, to help bulk system operators to address emerging ramping concerns. Numerical simulations demonstrate the effectiveness of the proposed model on when tested on a distribution system with several microgrids.

Mohsen Mahoor, Amin Khodaei

Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and data fusion techniques are integrated to estimate transformer loss of life. Using IEEE Std. C57.91-2011, a data synthesis process is proposed based on hourly transformer loading and ambient temperature values. This synthesized data is employed to estimate transformer loss of life by using Adaptive Network-Based Fuzzy Inference System (ANFIS) and Radial Basis Function (RBF) network, which are further fused together with the objective of improving the estimation accuracy. Among various data fusion techniques, Ordered Weighted Averaging (OWA) and sequential Kalman filter are selected to fuse the output results of the estimated ANFIS and RBF. Simulation results demonstrate the merit and the effectiveness of the proposed method.

Mohana Alanazi, Mohsen Mahoor, Amin Khodaei

The growing proliferation in solar deployment, especially at distribution level, has made the case for power system operators to develop more accurate solar forecasting models. This paper proposes a solar photovoltaic (PV) generation forecasting model based on multi-level solar measurements and utilizing a nonlinear autoregressive with exogenous input (NARX) model to improve the training and achieve better forecasts. The proposed model consists of four stages of data preparation, establishment of fitting model, model training, and forecasting. The model is tested under different weather conditions. Numerical simulations exhibit the acceptable performance of the model when compared to forecasting results obtained from two-level and single-level studies.

Mohana Alanazi, Mohsen Mahoor, Amin Khodaei

Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variable generation, thus highlighting the importance of accurate forecasting methods. This paper proposes a two-stage day-ahead solar forecasting method that breaks down the forecasting into linear and nonlinear parts, determines subsequent forecasts, and accordingly, improves accuracy of the obtained results. To further reduce the error resulted from nonstationarity of the historical solar radiation data, a data processing approach, including pre-process and post-process levels, is integrated with the proposed method. Numerical simulations on three test days with different weather conditions exhibit the effectiveness of the proposed two-stage model.

Alireza Majzoobi, Mohsen Mahoor, Amin Khodaei

The growing penetration of renewable generation in distribution networks, primarily deployed by end-use electricity customers, is changing the traditional load profile and inevitably makes supply-load balancing more challenging for grid operators. Leveraging the potential flexibility of existing microgrids, that is to help with supply-load balance locally, is a viable solution to cope with this challenge and mitigate existing net load variability and intermittency in distribution networks. This paper discusses this timely topic and determines the microgrid value of ramping based on its available reserve using a cost-benefit analysis. To this end, a microgrid ramping-oriented optimal scheduling model is developed and tested through numerical simulations to prove the effectiveness and the merits of the proposed approach in microgrid ramping valuation.

Hossein Valiyan Holagh, Tooraj Abbasian Najafabadi, Mohsen Mahoor

Conventionally, Selective Harmonic Elimination (SHE) method in 2-level inverters, finds best switching angles to reach first voltage harmonic to reference level and eliminate other harmonics, simultaneously. Considering Induction Motor (IM) as the inverter load, and wide DC bus voltage variations, the inverter must operate in both over-modulation and linear modulation region. Main objective of the modified SHE is to reduce harmonic torques through finding the best switching angles. In this paper, optimization is based on optimizing phasor equations in which harmonic torques are calculated. The procedure of this method is that, first, the ratio of the same torque harmonics is estimated, secondly, by using that estimation, the ratio of voltage harmonics that generates homogeneous torques is calculated. For the estimation and the calculation of the ratios motor parameter, mechanical speed of the rotor, the applied frequency, and the concept of slip are used. The advantage of this approach is highlighted when mechanical load and DC bus voltage variations are taken into consideration. Simulation results are presented under a wide range of working conditions in an induction motor to demonstrate the effectiveness of the proposed method.

Mohsen Mahoor, Alireza Majzoobi, Zohreh S. Hosseini et al.

Transformer lifetime assessments plays a vital role in reliable operation of power systems. In this paper, leveraging sensory data, an approach in estimating transformer lifetime is presented. The winding hottest-spot temperature, which is the pivotal driver that impacts transformer aging, is measured hourly via a temperature sensor, then transformer loss of life is calculated based on the IEEE Std. C57.91-2011. A Cumulative Moving Average (CMA) model is subsequently applied to the data stream of the transformer loss of life to provide hourly estimates until convergence. Numerical examples demonstrate the effectiveness of the proposed approach for the transformer lifetime estimation, and explores its efficiency and practical merits.

Seyed Mahmoud Salamati, Seyed Ali Salamati, Mohsen Mahoor et al.

Automotive industry is moving toward fully electric and hybrid electric vehicles. Accordingly, energy storage unit is one of the most important blocks in these electric drives. Battery stacks which contain a number of cells are being used for supplying the vehicles' energy. Charge equalization for series connected battery strings has a significant effect on battery life. In this paper, an adaptive model predictive controller (AMPC) is proposed to manage the cell equalizing process. The series connected cells' voltages and currents are collected, then leveraging Recursive Least Square (RLS) method, the future voltage samples for all of the cells are predicted. MPC controller specifies a sequence which results in the optimum balancing performance of the proposed circuit. Simulation results prove that using the suggested algorithm, the voltage set of the series cells has moved more uniformly.