A thorough analysis of basic electrical-systems considerations is presented. Guidance is provided in design, construction, and continuity of an overall system to achieve safety of life and preservation of property; reliability; simplicity of operation; voltage regulation in the utilization of equipment within the tolerance limits under all load conditions; care and maintenance; and flexibility to permit development and expansion. Recommendations are made regarding system planning; voltage considerations; surge voltage protection; system protective devices; fault calculations; grounding; power switching, transformation, and motor-control apparatus; instruments and meters; cable systems; busways; electrical energy conservation; and cost estimation.

IEEE Recommended Practice for Electric Power Distribution for Industrial Plants

Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity , each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.

Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction

This paper derives a nonlinear translational and rotational dynamic model of the quadrotor UAV. An original fractional‐order backstepping sliding mode control method for a typical quadroto...

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asjc.1946

Design of Fractional‐Order Backstepping Sliding Mode Control for Quadrotor UAV

Wind energy has been widely explored in recent decades and the integration of this renewable resource is increasing due to many factors, e.g., the need to mitigate climate change and reduce energy dependence on fossil fuels. Unfortunately, the stochastic characteristic of wind may have an impact on the reliability and power quality of electrical grids due to short-term power fluctuations. For wind power smoothing purposes, many researchers have been using energy storage systems (ESSs) as they perform extremely well, and are becoming less costly. In this context, this article presents a comprehensive review of the significant research conducted on the topic of wind power smoothing using high-power ESSs. Firstly, some energy storage technologies are discussed and compared, and a brief description of the main short-term applications of these technologies is provided. Afterwards, a literature review on wind power smoothing is conducted, showing evidence of the development of methods over the years. A bibliometric analysis is also presented, showing an increasing number of publications on this subject. Finally, this review indicates that more research is still needed to overcome wind power fluctuation issues and that further attention to topics such as hybrid ESSs and smoothing impacts on electrical grids should arise over the next years.

A review on wind power smoothing using high-power energy storage systems

The synchronous reluctance motor (SynRM) is a high-efficiency and low-cost motor with rugged structure. However, the nonlinear and time-varying control characteristics of the SynRM with high torque ripple confine the high-performance applications of this motor. Therefore, an intelligent backstepping control (BSC) using recurrent feature selection fuzzy neural network is proposed to construct a high-performance SynRM position servo drive system. First, the dynamic model of a vector control SynRM position servo drive is introduced. Second, a BSC system is designed for the tracking of the position reference. Since the lumped uncertainty of the SynRM position servo drive system is unavailable in advance, it is very difficult to design an effective BSC in practical applications. Moreover, the sign function in BSC will cause undesired chattering phenomenon, which will excite unknown dynamics and wear the ball bearing of the SynRM. To alleviate the existed difficulties in the BSC, the recurrent feature selection fuzzy neural network (RFSFNN) is proposed in this study to approximate an idea BSC. In addition, to compensate the possible approximated error of the RFSFNN, an improved adaptive compensator is augmented. The online learning algorithms of the RFSFNN are derived by using the Lyapunov stability method to assure asymptotical stability. Finally, the proposed control system is implemented in a 32-bit floating point digital signal processor. The effectiveness and robustness of the proposed intelligent BSC system are verified by some experimental results.

Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System

In order to construct a high-performance synchronous reluctance motor (SynRM) drive system, an intelligent maximum torque per ampere (MTPA) tracking control using a recurrent Legendre fuzzy neural network (RLFNN) is proposed in this study. First, a traditional MTPA (TMTPA) control system based on FOC is introduced. Since the reluctance torque of the SynRM is highly nonlinear and time-varying, the MTPA tracking control is very difficult to achieve by using the TMTPA control in practical applications. Then, an adaptive computed current (ACC) speed control using the proposed RLFNN for the MTPA tracking control of a SynRM drive system, which does not use a lookup table and can effectively obtain the optimal current angle command of MTPA online, is described in detail. The ACC speed control is applied to generate the stator current magnitude command, and an adaptation law is proposed to online adapt the value of a lumped uncertainty in the ACC control. Moreover, the adaptation law is derived using the Lyapunov stability theorem to guarantee the asymptotic stability of the ACC speed control. Furthermore, the proposed RLFNN is employed to produce the incremental command of the current angle. In addition, the ACC speed control and RLFNN are implemented in a TMS320F28075 32-bit floating-point digital signal processor for a 4 kW SynRM drive system. Finally, the robustness and effectiveness of the proposed intelligent MTPA tracking control are verified by some experimental results.

Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network

An adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high-performance six-phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field-oriented six-phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six-phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six-phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.

Adaptive Backstepping Control of Six-Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

A digital signal processor (DSP)-based intelligent sliding-mode control (SMC) is proposed for the position control of a six-phase permanent magnet synchronous motor (PMSM) drive system installed in an electric power steering (EPS) system in this study. First, the dynamic mathematical model of the EPS system is derived by the Lagrangian dynamics. Since the EPS system is a nonlinear and time-varying system, the control accuracy is very sensitive to the parameter variations and external disturbances. Therefore, a SMC is developed for the position control of the EPS system. However, the upper bound of the uncertainties is difficult to obtain in advance and the choice of switching control gain in SMC is vital but time-consuming and may cause undesired chattering phenomenon. Hence, an intelligent SMC with a novel recurrent wavelet fuzzy neural network (ISMC-RWFNN) is proposed, in which a recurrent wavelet fuzzy neural network (RWFNN) is adopted as an uncertainty estimator to overcome the aforementioned disadvantage of SMC. Moreover, a robust compensator is employed to reduce the estimation error. In addition, the adaptive learning algorithms for the online training of the RWFNN are derived using the Lyapunov theorem and Taylor series. Finally, the proposed ISMC-RWFNN to control the position of a six-phase PMSM drive system for the EPS system is implemented in a 32-bit floating-point DSP, and some experimental results are provided to verify its effectiveness.

Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

To develop a high-performance synchronous reluctance motor (SynRM) drive system, a novel maximum power factor control (MPFC) using a current angle controller with stator resistance and stator flux estimators is proposed. First, a traditional maximum power factor control system using a saliency ratio of the SynRM to generate a fixed current angle command is described. Since the saliency ratio requires offline prepreparation and cannot be adjusted automatically, it is difficult to improve the performance of the MPFC in different operating regions because of the increasing of manufacturing cost and time-consumption. Therefore, an intelligent-maximum power factor searching control (MPFSC) using a recurrent Chebyshev fuzzy neural network (RCFNN) current angle controller is developed for the speed control of a SynRM. In order to search the online optimal power factor (PF) points of the SynRM under different operating conditions, the RCFNN current angle controller is designed to produce the compensated current angle command. Moreover, a proportional-integral speed controller is adopted to generate the stator current magnitude command, and the proposed intelligent-MPFSC is employed to generate the current angle command. Furthermore, the proposed intelligent-MPFSC system is implemented in a 32-bit floating-point TMS320F28075 digital signal processor. Finally, from the experimental results, the current angle commands of the optimal PF can be effectively obtained online at different speed operating commands with varied load torque.

Shih-Gang Chen

Papers

Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System

Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network

Adaptive Backstepping Control of Six-Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System

Intelligent Maximum Power Factor Searching Control Using Recurrent Chebyshev Fuzzy Neural Network Current Angle Controller for SynRM Drive System