This study examines the critical role of precise rotor position estimation in field-oriented control (FOC) of current source inverter (CSI)-fed Permanent Magnet Synchronous Motor (PMSM) drives for submersible pump applications. Despite advancements, challenges such as inverter nonlinearity and parameter variations introduce significant errors in rotor position estimation under dynamic and steady state conditions. This adversely affects the reliability and efficiency of the drive system. The proposed adaptive PLL aims to optimize PMSM acceleration by dynamically adjusting PLL bandwidth, effectively mitigating estimation errors caused by various non-ideal factors in steady state condition. Simulation and experimental results validate the effectiveness of the proposed approach.

In this article, a model-based real-time approach is proposed to disaggregate a feeder-level load and estimate the total energy of plug-in electric vehicles (PEVs), considering the controlled charging mode and the vehicle-to-grid capability of PEVs. To this end, aggregate demand of load categories participating at the feeder-head as well as the total energy of PEVs are analytically modeled. Then, the state-space representation of the system according to the mentioned models and their relations is proposed. Finally, a Kalman filter-based method is applied to disaggregate the feeder-level load into the aggregate demand of load categories and estimate the total energy of PEVs in real time. The accuracy and complexity of the proposed method are compared with two model-free methods, i.e., a nonlinear autoregressive with exogenous inputs-based shallow learning model and a long short-term memory-based deep learning approach, by using real data. They employ distribution substation measurements along with charging data of a very small subset of PEVs. The comparison results indicate that although the artificial neural network-based methods can effectively represent the nonlinear behavior of the feeder-level load and its components, the Kalman filter-based method significantly improves the PEVs’ total energy estimation by taking into account modeling and measurement uncertainties.

Charging demand of plug-in electric vehicles (PEVs) can cause reliability and operational challenges in power distribution systems. The aggregated charging control of PEVs in the parking lots (PLs) may alleviate the challenges, if the place and size of PEV PLs are optimally determined. This paper develops a stochastic framework for finding optimal location and sizing of PLs as well as optimal charging profile of PEV PLs with the vehicle to grid capability in a reconfigurable distribution system. The main aims are to reduce distribution system losses and enhance network reliability, subject to numerous constraints of the power distribution system, PLs, and PEVs. To guarantee the global optimum solutions, the proposed optimization problem is linearized to achieve a mixed-integer linear program model. Furthermore, kernel density estimator (KDE) is presented to model the temporal uncertainties associated with PEV owners’ behavior with small number of iterations and no necessary assumptions. Various scenarios and sensitivity analysis are conducted to show the efficacy of the proposed method.

One of the critical challenges in wind energy development is the uncertainty quantification. Prior knowledge about the wind speed in look-ahead times in shape of probabilistic information plays a pivotal role in the optimal operation and planning in the electrical networks. In this article, we design a deep learning-based approach to characterize the probability density function (PDF) of the wind for the next hours. The proposed method is directly applicable to raw data and directly constructs PDFs and enhances the level of accuracy and reliability as well as computational efficiency. Furthermore, we utilize the convolutional neural network to enhance learning spatial features. To provide a better understanding of temporal features, a recurrent neural network, called gated recurrent unit, is utilized. To directly construct PDFs, a gradient-based loss function is proposed, and the training procedure is modified. The effectiveness and superiority of the proposed probabilistic wind speed forecasting are verified by two actual datasets, i.e., London, England, and Shiraz, Iran, and comprehensive numerical results validate the performance of the proposed approach in comparison with several state-of-the-art and previously investigated approaches in terms of sharpness, accuracy, and reliability.

The aim of this article is to explore the multiagent reinforcement learning approach for residential multicarrier energy management. Defining the multiagents system not only enhances the possibility of dedicating separate demand response programs for different components but also accelerates the computational calculations. We employ the Q-learning to provide the optimum solution in solving the presented residential energy management problem. Furthermore, to address uncertainties, a scenario-based method with the real data and proper probability density functions is used. Deterministic and stochastic numerical calculations are made to justify the effectiveness and robustness of the proposed method. The simulated results indicate that the application of the proposed reinforcement learning-based method leads to lower cost schemes for consumers rather than the conventional optimization-based energy management programs.

Realizing the factors involved in power system outages can be effective in reliability improvement. In this article, we analyze the distribution power network outage data to find dominant factors in occurring vegetation-, animal-, and equipment-related outages. After their integration, real outage, weather, and load as the input data are used to extract associated features. In this article, visualization techniques are initially utilized to show the impact of features on the outage occurrence and then association rule mining is used to find factors correlated with each outage type as well as each other. Association rules are mined using Apriori technique, considering the chi-square and lift index as the measures of interestingness. The outage analyses are also performed for each equipment separately to find the associated rules. The results showing the effectiveness and validity of the proposed method to identify the factors connected with outage occurrences can be used for future planning and the operation schedule of distribution power networks.