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Application of echo state networks for estimating voltage harmonic waveforms in power systems considering a photovoltaic system

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IET RENEWABLE power GENERATION 2017年第13期11卷 1688-1694页

作者： Deihimi, Ali Rahmani, Abolfazl Bu Ali Sina Univ Dept Elect Engn Shahid Fahmideh St Hamadan *** Iran

An estimator based on echo state networks (ESNs) is presented in this study to estimate voltage harmonic distortion waveforms at non-monitored sensitive loads using current and voltage at a monitored location. Since distributed generations such as photovoltaic systems have played a special role in distribution networks so they are considered in this study and their effects on a harmonic voltage waveform estimator are evaluated. Voltage harmonics are considered as the main type of waveform distortion in a power quality approach. Voltage and current harmonics cause an increase in the malfunction of electrical equipment in power systems. To detect and analyse the voltage harmonics, installation of power quality monitors (PQMs) at all buses is not economical. Reducing the cost associated with the monitoring procedure can be achieved by optimising the number of PQMs to be installed. The presented technique is examined on an IEEE 37-bus network and results from the studies indicate an acceptably high accuracy of ESN estimator.

关键词： distributed power generation photovoltaic power systems harmonic distortion power supply quality power system harmonics distribution networks recurrent neural nets power engineering computing echo state networks power systems photovoltaic system voltage harmonic distortion waveform estimation nonmonitored sensitive loads distributed generations distribution networks power quality approach current harmonics voltage harmonics electrical equipment malfunction power quality monitors PQMs cost reduction IEEE 37-bus network ESN estimator

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THEORY OF FLUX-PENETRATION INTO LAMINATED IRON AND ASSOCIATED LOSSES

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PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1977年第7期124卷 659-664页

作者： CARPENTER, CJ UNIV LONDON IMPERIAL COLL SCI & TECHNOLDEPT ELECT ENGNLONDON SW7 2AZENGLAND

The penetration of leakage flux into a laminated machine or transformer core is governed by the currents induced in the steel, and poses an anisotropic and 3-dimensional field problem which is also severely nonlinear. It is shown that the differential equations can be formulated in terms of an electric vector potential T, and magnetic scalar potential Ω, and that the solution can be expressed in the form of three different characteristic modes, two associated with the core surfaces and the third describing the flux penetration into the interior. This form of solution provides both a qualitative and quantitative picture of the 3-dimensional flux paths, and eddy current loss distribution, and also a practicable means of allowing for saturation. The result can be expressed in the form of a simple network model that is well suited to numerical solution by computer.

关键词： transformers electric vector potential transformer core flux paths losses Transformers and reactors electrical engineering computing magnetic scalar potential three dimensional field problem turbogenerators associated losses currents laminated iron laminated machine magnetic flux power engineering computing computer network model numerical solution leakage flux penetration theory saturation a.c. machines eddy current loss distribution numerical methods Numerical analysis steel

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Convolutional Neural Network-Based Intelligent Protection Strategy for Microgrids

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第7期14卷 1177-1185页

作者： Bukhari, Syed Basit Ali Kim, Chul-Hwan Mehmood, Khawaja Khalid Haider, Raza Zaman, Muhammad Saeed Uz Natl Univ Sci & Technol US Pak Ctr Adv Studies Energy Islamabad Pakistan Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea Balochistan Univ Engn & Technol Dept Elect Engn Khuzdar Pakistan

Microgrids experience significantly different fault currents in different operating scenarios, which make microgrid protection challenging. Existing intelligent protection schemes rely on the extraction of appropriate fault features using statistical parameters. The selection of these features is difficult in a microgrid because of its various operating scenarios. This study develops a convolutional neural network-based intelligent fault protection strategy (CNNBIPS) for microgrids that inherently integrates the feature extraction and classification process. The proposed strategy is directly applicable to three-phase (TP) current signals;thus, it does not require any separate feature extractor. In the proposed CNNBIPS, TP current signals sampled by the protective relays are used as an input to three different CNNs. The CNNs apply convolution and pooling operations to extract the features from the input signals. Then, fully connected layers of the CNNs employ the features to develop fault-type, phase, and location information. To analyse the efficacy of the proposed design, we execute exhaustive simulations on a standard microgrid test system. The results confirm the effectiveness of the proposed strategy in terms of detection accuracy, security, and dependability. Moreover, comparisons with previous methods show that the proposed approach outperforms the existing microgrid protection schemes.

关键词： power generation control power distribution protection fault currents distributed power generation fault diagnosis feature extraction power generation protection power engineering computing neural nets power distribution faults CNNBIPS microgrids different fault currents different operating scenarios microgrid protection challenging intelligent protection schemes appropriate fault features statistical parameters convolutional neural network-based intelligent fault protection strategy feature extraction three-phase current signals separate feature extractor TP current signals protective relays different CNNs convolution pooling operations input signals fault-type standard microgrid test system existing microgrid protection schemes

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Evaluation method for moisture content of oil-paper insulation based on segmented frequency domain spectroscopy: From curve fitting to machine learning

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2021年第6期15卷 517-526页

作者： Yao, Huanmin Mu, Haibao Ding, Ning Zhang, Daning Liang, ZhaoJie Tian, Jie Zhang, Guanjun Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China Shenzhen Power Supply Co Ltd Shenzhen Guangdong Peoples R China

In recent years, frequency domain spectroscopy (FDS) is often used to evaluate oil paper insulation state in power transformer bushing. But it is still very difficult to evaluate the moisture content accurately and quickly. In order to solve this problem, this paper proposes an intelligent algorithm based on random forest regression (RFR) to construct an efficient evaluation method through segmented FDS curves. Furthermore, the characteristics of FDS curves were studied and the intelligent method was compared with support vector regression (SVR) and deep neural networks (DNN). The results show that the dielectric loss, the real part and imaginary part of complex capacitance all move upward with the moisture increasing, so they can be used as the input feature of the evaluation model;The moisture content evaluation accuracy of the RFR model in the whole frequency band is higher than that of SVR and DNN models;With the increase of lower cut off frequency (FDS test stop frequency), the FDS test time is greatly shortened, and the accuracy of the RFR model can still meet the evaluation requirements. Therefore, the data in a compromise frequency band can be used to evaluate the moisture content of oil paper insulation accurately and quickly.

关键词： Organic insulation Interpolation and function approximation (numerical analysis) Interpolation and function approximation (numerical analysis) power engineering computing Transformers and reactors Regression analysis Regression analysis Neural nets Support vector machines Other learning models (inc. Naive Bayes)

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INTEGRATED EXCITATION AND TURBINE CONTROL IN A MULTIMACHINE power-SYSTEM

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IEE PROCEEDINGS-C GENERATION TRANSMISSION AND DISTRIBUTION 1989年第6期136卷 331-340页

作者： WALKER, PAW SERAG, AM ABDALLA, OH UNIV HELWAN FAC ENGN & TECHNOLDEPT ELECT MACHINES & POWER ENGNCAIROEGYPT

This paper is concerned with modelling and optimal control studies, by computer simulation, of a multimachine power system comprising three interconnected generator units. The most effective generating unit, for the application of control, is determined by using eigenvalue analysis. A multi-input/multi-output linear model of the system, as seen from this unit, is obtained by least-squares identification. On the basis of this model a multivariable controller is designed, by minimising a quadratic performance index, using the dynamic programming procedure. This local controller employs output feedback, and provides integrated control of the exciter and turbine-valve inputs. Multivariable controllers are also designed and applied to the other generating units, to allow comparative system performance to be examined. System behaviour is assessed in relation to small-and large-disturbance studies. Simulation results include responses to three-phase short circuits, and dynamic and transient stability boundaries.

关键词： turbine control turbine-valve inputs least-squares identification multivariable control systems power engineering computing integrated excitation control power systems power system computer control 3-phase short circuits quadratic performance index dynamic programming interconnected generator units Control of electric power systems multimachine power system computer simulation power systems Control engineering computing eigenvalue analysis controllers digital simulation stability boundaries multivariable controller turbines

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Optimal control strategy for solid oxide fuel cell-based hybrid energy system using deep reinforcement learning

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IET RENEWABLE power GENERATION 2022年第5期16卷 912-921页

作者： Chen, Tao Gao, Ciwei Song, Yutong Southeast Univ Sch Elect Engn Nanjing Peoples R China

This paper proposes a self-adaptive control strategy for solid oxide fuel cell (SOFC) based hybrid energy system using deep reinforcement learning (DRL) techniques. Highly efficient use of hydrogen in a hybrid energy system with the aid of SOFC could create a new paradigm of renewable energy ecosystem and a series of operation principles. Instead of modeling the energy system operation decision-making process as an optimization problem, a DRL framework is used to seek the optimal control strategy with consideration of various physical constraints in the SOFC components and hybrid energy system operation. Specifically, a deep deterministic policy gradient (DDPG) algorithm is used to solve the operation problem and provide the optimal policy guiding the control actions of a tubular SOFC stack, which involves various dynamic characteristics besides the electric measurement. The learnt control strategy may not produce the best result every time, but can guarantee the ultimate benefit in a non-deterministic way in the long-term operation.

关键词： power engineering computing renewable energy ecosystem self-adaptive control strategy SOFC components deep learning (artificial intelligence) control actions optimisation deep deterministic policy gradient algorithm learnt control strategy hybrid energy system operation optimal control adaptive control operation problem solid oxide fuel cells solid oxide fuel cell-based hybrid energy system optimal policy optimization problem deep reinforcement learning techniques reinforcement learning decision making tubular SOFC stack optimal control strategy gradient methods energy system operation decision-making process operation principles

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MICROPROCESSOR AND DIGITAL ICS FOR MOTION CONTROL

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PROCEEDINGS OF THE IEEE 1994年第8期82卷 1140-1163页

作者： HOANG, LH Dept. of Electr. Eng. Laval Univ. Que.

This paper reviews the recent developments in microprocessors and digital IC's with a special attention to applications to motion control area. First, the use of microprocessors and digital IC's in the control of electrical motor drives is considered. General-purpose microprocessors and microcontrollers are then presented with emphasis on features that are necessary for the implementation of real-time control systems. High-performance advanced processors such as digital signal processors (DSP's), reduced-instruction-set computing (RISC) processors, and parallel processors are studied. Development and debugging tools required for developing sophisticated microprocessor-based control systems are considered. The capabilities of application-specific integrated circuits (ASIC's) in motion control and their design process are examined. Typical application examples illustrating recent research works in digital motion control using microprocessors and digital IC's are presented. The potentials of new devices are considered for application to high-performance drive systems. Trends for future development of the microprocessor technology for motion control applications are projected.

关键词： ASIC DSP RISC application specific integrated circuits application-specific integrated circuits control systems debugging tools development tools digital IC digital integrated circuits digital signal processing chips digital signal processors electric drives electric motor drives electric motors machine control microcomputer applications microcontrollers microcontrollers microprocessors motion control parallel processing parallel processors position control power engineering computing real-time control systems real-time systems reduced instruction set computing reduced-instruction-set computing

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Meeting IEC and ER G4/5 harmonic standards in nine-level cascaded H-bridge inverters using an improved SHM-PAM scheme

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IET power ELECTRONICS 2020年第18期13卷 4395-4406页

作者： Kundu, Sourabh Banerjee, Subrata Ronanki, Deepak Williamson, Sheldon S. Natl Inst Technol NIT Durgapur Dept Elect Engn Durgapur 713209 India Univ Ontario Inst Technol Dept Elect Comp & Software Engn Oshawa ON L1G 0C5 Canada Indian Inst Technol Roorkee Dept Hydro & Renewable Energy Roorkee 247667 Uttarakhand India

This study introduces a new switching scheme for nine-level cascaded H-bridge (CHB) inverter to comply with the harmonic standards (IEC 61000-2-12, IEC 61000-3-6, and ER G4/5) using an improved selective harmonic minimisation pulse amplitude modulation (SHM-PAM) scheme. In this scheme, the optimised switching instances and variable DC-link voltages are determined by solving some new constraints based cost functions using particle swarm optimisation (PSO) technique. The theoretical analysis and optimisation results of the proposed modulation scheme are validated through MATLAB simulations and experimentally on a laboratory-scale prototype of CHB inverter. The proposed modulation scheme utilises a minimal number of switching instances in a fundamental period and optimisation variables in the problem formulation in comparison to conventional SHE-PWM and SHE-PAM schemes. The key performance of the proposed scheme in terms of harmonic and loss analysis is evaluated over the wide range of the power factors and modulation index. Finally, the suitability of the proposed scheme is tested for the closed-loop speed control of a 5 hp, 415 V three-phase induction motor.

关键词： IEC standards harmonics suppression closed loop systems particle swarm optimisation invertors power system harmonics pulse amplitude modulation induction motors power engineering computing power factor bridge circuits velocity control improved selective harmonic minimisation pulse amplitude modulation scheme switching instances variable DC-link voltages particle swarm optimisation CHB inverter harmonic loss analysis modulation index nine-level cascaded H-bridge inverter improved SHM-PAM scheme PSO technique MATLAB simulations power factors closed-loop speed control ER G4-5 harmonic standards IEC 61000-2-12 harmonic standards IEC 61000-3-6 harmonic standards three-phase induction motor power 5 0 hp voltage 415 0 V

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Monitoring and identification of metal-oxide surge arrester conditions using multi-layer support vector machine

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IET GENERATION TRANSMISSION & DISTRIBUTION 2015年第16期9卷 2501-2508页

作者： Khodsuz, Masume Mirzaie, Mohammad Babol Univ Technol Dept Elect & Comp Engn Babol Sar Iran

Metal-oxide surge arresters (MOSAs) are essential equipments for power system protection and devices from lightning and switching transient overvoltages. Therefore, their operating condition and diagnosis are very important. In this study, a multi-layer support vector machine (SVM) classifier has been used for MOSA conditions monitoring based on experimental tests. Three features are extracted based on the test results for determining surge arresters operating conditions including clean virgin, ultraviolet (UV) aged clean surface, surface contaminations after and before UV housing ageing, and degraded varistors along active column. Then, the multi-layer SVM classifier is trained with the training samples, which are extracted by the above data processing. Finally, the five fault types of surge arresters are identified by this classifier. The test results show that the classifier has an excellent performance on training speed and reliability which confirm the high applicability of introduced features for correct diagnostic of surge arresters conditions.

关键词： arresters overvoltage protection lightning protection power system protection support vector machines power engineering computing switching transients pattern classification surface contamination varistors fault diagnosis condition monitoring metal oxide surge arrester multilayer support vector machine power system protection power system devices lightning overvoltage switching transient overvoltage operating condition multilayer SVM classifier MOSA conditions monitoring ultraviolet aged clean surface surface contamination UV housing ageing varistors fault type identification reliability

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Islanding detection technique based on relevance vector machine

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IET RENEWABLE power GENERATION 2016年第10期10卷 1607-1615页

作者： Makwana, Yogesh Bhalja, Bhavesh R. Indian Inst Technol Roorkee Dept Elect Engn Roorkee 247667 Uttrakhand India

This study present a new islanding detection technique based on relevance vector machine (RVM) containing various types of distributed generations (DGs). The proposed scheme is based on utilising negative sequence component of current (I-2), acquired at the terminal of the target DG. Various islanding and non-islanding events with variable real and reactive power, change in network topology and diverse X/R ratio have been generated by modelling IEEE 34 bus system in real time digital simulator (RTDS (R)/RSCAD) environment. The samples of I-2 for one cycle duration from the inception of islanding/non-islanding events are given as input to the proposed RVM classifier. The proposed classifier is able to discriminate between islanding and non-islanding events with an accuracy of 98.62% by utilising only 40% of the total dataset in training. Moreover, it is capable to classify islanding condition rapidly and correctly even with perfect power balance situation. Furthermore, it remains immune to change in network configuration/entirely different network and X/R ratio of various types of DGs. Comparative analysis of the proposed scheme in terms of accuracy, testing time and number of relevance vectors (RVs) with the existing schemes shows its superiority in discriminating islanding situation with non-islanding events.

关键词： distributed power generation IEEE standards digital simulation power engineering computing support vector machines islanding detection technique relevance vector machine distributed generations negative sequence component reactive power network topology IEEE 34 bus system real time digital simulator RTDS/RSCAD environment RVM classifier comparative analysis

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