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NEW PWM SWITCHING STRATEGY FOR MICROPROCESSOR CONTROLLED INVERTER DRIVES

IEE PROCEEDINGS-B ELECTRIC power APPLICATIONS 1986年第4期133卷 237-254页

作者： BOWES, SR MIDOUN, A Department of Electrical and Electronic Engineering University of Bristol Bristol UK

A new high-voltage suboptimal (HVSO) pulse-width modulation (PWM) switching strategy is presented which can be used at high fundamental PWM voltages to achieve a smooth, transient-free, low harmonic distortion, transition from the PWM to quasisquare-wave mode. The design philosophy underlying the development of the new HVSO strategy is aimed at reproducing the special features and desirable characteristics of the optimised PWM strategy, using a simple and efficient microprocessor implementation. The full theoretical development of the new HVSO strategy is presented; and experimental results, from a microprocessor controlled PWM inverter drive, are provided which confirm this theoretical development. It is shown that the new HVSO strategy is superior to all existing PWM strategies, and can be implemented simply and efficiently on a microprocessor controller with only minor modifications to the existing PWM microprocessor implementations. In addition, a new hybrid PWM strategy is proposed and demonstrated, which combines the HVSO strategy with a previously developed suboptimal strategy. It is shown that this new hybrid PWM strategy can be used to cover the complete voltage/frequency range of the drive system up to and including the quasisquare wave mode, with minimised total harmonic distortion.

关键词： Control engineering computing high voltage suboptimal pulse width modulation electric drives Drives PWM inverter drive switching PWM switching strategy HVSO strategy computerised control power convertors and power supplies to apparatus power engineering computing microprocessor controlled inverter drives invertors pulse width modulation Control of electric power systems

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Edge-cloud collaborative architecture based multi-time scales rolling optimization of regional integrated electrical and natural gas energy system considering wind power uncertainty

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IET GENERATION TRANSMISSION & DISTRIBUTION 2021年第19期15卷 2684-2709页

作者： Cao, Zhiao Zhao, Qiang Wang, Jia Wang, Jinkuan Han, Yinghua Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ 143 Taishan Rd Qinhuangdao 066004 Hebei Peoples R China

With the serious energy crisis and speedy development of natural gas utilization, regional integrated electrical and gas energy system is an efficient way to improve energy potency. This paper studies the energy scheduling of integrated electrical and gas energy system with bi-directional energy conversion at distribution network level, considering wind power uncertainty. To realize independent autonomy of energy networks, meantime adjusting scheduling strategies of integrated energy system from system-level perspective, innovative edge-cloud collaborative operation architecture is proposed. In edge servers, electrical and gas networks are optimized severally for scheduling strategies. In cloud server, the uncertainty of wind power are processed with robust optimization method, strategies of energy conversion units are updated for re-optimization. Cooperated with edge-cloud architecture, a novel multi-time scales rolling optimization framework is proposed, electrical and gas networks are optimized under day-ahead time scale in corresponding edge servers, and electrical network is further adjusted under intraday time scale in electrical edge server. Case studies show that edge-cloud collaborative operation architecture can ensure the practical feasibility of optimization, which is more suitable for energy scheduling of integrated electrical and gas energy system. Multi-time scales rolling optimization framework have significant efficiency on reducing the impact of wind power uncertainty.

关键词： Wind power plants Optimisation techniques Optimisation techniques power system management, operation and economics Distribution networks power engineering computing

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STATE FORECASTING IN ELECTRIC-power SYSTEMS

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IEE PROCEEDINGS-C GENERATION TRANSMISSION AND DISTRIBUTION 1983年第5期130卷 237-244页

作者： DASILVA, AML DOCOUTTO, MB DEQUEIROZ, JF FED UNIV RIO DE JANEIRO DEPT ELECT ENGNRIO DE JANEIRORJBRAZIL

The state vector of a power system varies with time owing to the dynamic nature of system loads. Therefore, it is necessary to establish a dynamic model for the time evolution of the state vector. The dynamic state estimation approach consists of predicting the state vector based on past estimations, followed by a filtering process performed when a new set of measurements is available. This paper presents a new algorithm for forecasting and filtering the state vector, using exponential smoothing and least-squares estimation techniques. The proposed algorithm is compared with another one based on standard Kalman filtering theory. Numerical results showing the performance for both dynamic estimators under different operational conditions are presented and discussed. Detection and identification of multiple bad data are also included. The new dynamic estimator exploiting state forecasting is extremely useful to real-time monitoring of power systems.

关键词： eigenvalues and eigenfunctions least squares approximations Simulation, modelling and identification dynamic model state estimation filtering power engineering computing Algebra Interpolation and function approximation (numerical analysis) state vector electric power systems least-squares estimation techniques real-time monitoring time evolution algorithm identification multiple bad data power system analysis computing power system management, operation and economics forecasting Numerical analysis exponential smoothing

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Deep learning-based SCUC decision-making: An intelligent data-driven approach with self-learning capabilities

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第4期16卷 629-640页

作者： Yang, Nan Yang, Cong Xing, Chao Ye, Di Jia, Junjie Chen, Daojun Shen, Xun Huang, Yuehua Zhang, Lei Zhu, Binxin China Three Gorges Univ Hubei Prov Engn Res Ctr Intelligent Energy Techno Yichang 443002 Peoples R China Yunnan Power Grid Co Ltd Eleci Power Res Inst Kunming 650217 Yunnan Peoples R China State Grid Fujian Elect Power Co Ltd Fuzhou Power Supply Co Fuzhou 350009 Fujian Peoples R China State Grid Jiangxi Elect Power Co Ltd Yingtan Power Supply Branch Yingtan 335000 Jiangxi Peoples R China State Grid Hunan Elect Power Co Ltd Elect Power Res Inst Changsha 410007 Hunan Peoples R China Tokyo Univ Agr & Technol Dept Mech Syst Engn Koganei Tokyo 1848588 Japan

This paper proposes an intelligent Deep Learning (DL) based approach for Data-Driven Security-Constrained Unit Commitment (DD-SCUC) decision-making. The proposed approach includes data pre-processing and a two-stage decision-making process. Firstly, historical data is accumulated and pre-processed. Then, the DD-SCUC model is created based on the Gated Recurrent Unit-Neural Network (GRU-NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two-stage decision-making process outputs the decision results based on various applications and scenarios. This approach has self-learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118-bus test system and a real power system from China showed that compared with deterministic Physical-Model-Driven (PMD)-SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.

关键词： decision making power system security power generation dispatch power generation scheduling historical data sets self-learning capabilities intelligent data-driven approach Deep learning-based SCUC decision-making recurrent neural nets Data-Driven Security-Constrained Unit Commitment decision-making deep learning (artificial intelligence) DD-SCUC model SCUC decisions two-stage decision-making process data pre-processing power engineering computing mapping model learning (artificial intelligence)

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INTELLIGENT ONLINE DECISION-SUPPORT FOR DISTRIBUTION-SYSTEM CONTROL AND OPERATION

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IEEE TRANSACTIONS ON power SYSTEMS 1995年第4期10卷 1820-1827页

作者： BURT, GM MCDONALD, JR KING, AG SPILLER, J BROOKE, D SAMWELL, R S WESTERN ELECT BRISTOLAVONENGLAND

Significant improvements have been taking place in the power and extent of the monitoring and control systems employed by electrical power utilities, with the result that strategic decisions are having to be made concerning the use of the increased data now available. The evolutionary changes being made to power systems are introducing increased expectations of the performance of system management and control. However, more effective system control can only be exercised if the increased volume of data is matched by an increased capacity to interpret system conditions from the control room. The decision support system described in this paper makes use of expert system techniques in order to provide intelligent reasoning based on the incoming alarm data. Both the readily available benefits, and the advance towards the next generation of power systems which this type bf system will afford shall be presented along with a description of the decision support system.

关键词： Control systems power system restoration power systems SCADA systems power system dynamics power system management power system control Expert systems power engineering computing System performance

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INTEGRATION OF HVDC LINKS WITH FAST-DECOUPLED LOAD-FLOW SOLUTIONS

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PROCEEDINGS OF THE INSTITUTION OF ELECTRICAL ENGINEERS-LONDON 1977年第5期124卷 463-468页

作者： ARRILLAGA, J BODGER, P UNIV CANTERBURY DEPT ELECT ENGNCHRISTCHURCHNEW ZEALAND

A model of the high-voltage direct-current transmission link suitable for incorporation in fast decoupled a.c. load-flow programs is described. The model is not restricted to a particular control made and provision is made to alter the control equations according to prespecified constraints for the variables. The versatility of decoupled programs is increased with this addition and it is shown that their reliability and computational efficiency are maintained.

关键词： DC power transmission transmission link d.c. transmission HVDC decoupled programs control equations Network and transmission line calculations integration with fast decoupled load flow solutions reliability power system analysis computing high voltage direct current power engineering computing computational efficiency load distribution

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Fault location on a series-compensated three-terminal transmission line using deep neural networks

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第6期12卷 746-754页

作者： Mirzaei, Mahdi Vahidi, Behrooz Hosseinian, Seyed Hossein Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran

In this study, discrete wavelet transform (DWT) and deep neural network (DNN) are utilised for fault location in a series-compensated three-terminal transmission line. The series compensation causes challenges in fault location schemes of the three-terminal transmission lines. The presented fault location method has been extensively tested using the SIMULINK model of a three-terminal transmission line. Features extracted from synchronous measurements of fault currents at the three terminals using DWT are fed to the DNN. Faulted section determination and fault distance calculation are carried out using a single intelligent network simultaneously. Faulted section is determined with 100% accuracy, and the efficiency of algorithm is validated for symmetrical and unsymmetrical faults, and different values of fault resistance, inception angle, and location. The accuracy of the algorithm is acceptable for large fault resistances (above 100) and fault inception angles near zero. Total mean error for test data is 0.0458% which is much improved with respect to other similar works.

关键词： fault currents feature extraction discrete wavelet transforms power engineering computing neural nets power transmission faults power transmission lines fault location series-compensated three terminal transmission line deep neural network discrete wavelet transform DWT DNN fault location scheme SIMULINK model feature extraction synchronous measurement fault distance calculation intelligent network fault resistance fault current

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Co-ordinated control of generator excitation and STATCOM for rotor angle stability and voltage regulation enhancement of power systems

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 2002年第6期149卷 659-666页

作者： Cong, L Wang, Y Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

A co-ordinated control scheme for STATCOM and generator excitation to achieve transient stability, damping and voltage regulation enhancement of power systems is presented. First, the nonlinear model of STATCOM installed in a power system is derived. Then, using the feedback linearisation technique, the nonlinearities of the generator and the STATCOM model are alleviated. With the help of robust control theory, the variation of system structure, the parameter uncertainties and the interconnection between the generator and STATCOM are taken into consideration in the controller design. Only local measurements are required. The performance of the proposed control scheme is evaluated through a real time test by means of the real time digital simulator (RTDS). The results present comparisons of the system performance with different controllers, over a wide range of operating conditions. It is shown that the proposed co-ordinated control can provide better stability, voltage control and damping than other schemes.

关键词： parameter uncertainties STATCOM power system transient stability operating conditions co-ordinated control scheme power engineering computing feedback damping real time digital simulator rotor angle stability generator excitation real-time systems a.c. machines Other power apparatus and electric machines robust control theory stability nonlinear model digital simulation AC generators rotors power systems linearisation techniques machine control feedback linearisation technique power system management, operation and economics interconnection Control of electric power systems local measurements power system control Voltage control transient stability static VAr compensators robust control voltage control power system simulation real time test Stability in control theory voltage regulation enhancement power system control

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Field programmable gate array implementation of a fault location system in transmission lines based on artificial neural networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第2期5卷 191-198页

作者： Ezquerra, J. Valverde, V. Mazon, A. J. Zamora, I. Zamora, J. J. Univ Basque Country Elect & Telecommun Dept Fac Engn Bilbao Bilbao 48013 Spain Univ Basque Country Dept Elect Engn Fac Engn Bilbao Bilbao 48013 Spain

In the field of electrical engineering there are many areas where artificial neural networks (ANNs) are being applied. ANNs can be modelled and implemented by means of the execution of specific software in sequential computers. Nevertheless, if a real-time application wants to be developed, it is necessary to study how to use its massively parallel processing capacity by a specific electronic system. This study presents the methodology, the design and the field programmable gate array implementation process of a fault classification and location prototype for overhead transmission lines, based on ANNs.

关键词： Overhead power lines fault classification field programmable gate arrays Logic circuits Neural computing techniques power engineering computing sequential computers fault location artificial neural networks power transmission faults Logic and switching circuits field programmable gate array fault location system electrical engineering power engineering computing power overhead lines neural nets overhead transmission lines

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Evaluation and forecasting of solar radiation using time series adaptive neuro-fuzzy inference system: Seoul city as a case study

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IET RENEWABLE power GENERATION 2019年第10期13卷 1711-1723页

作者： Alsharif, Mohammed H. Younes, Mohammad K. Sejong Univ Dept Elect Engn Coll Elect & Informat Engn Seoul South Korea Philadelphia Univ Dept Civil Engn Amman Jordan

Given that solar radiation is unpredictable, an accurate solar energy prediction model must be developed. This study aimed to evaluate the changes in solar radiation over the past 37 years in Seoul city. The capability of the adaptive neuro-fuzzy inference system (ANFIS) to forecast solar radiation using chaotic time series inputs was analysed. Results demonstrate the capability of ANFIS to provide a relatively good monthly solar prediction model with a normalised root mean square error of 0.22%, a root mean square error of 55.4, and a coefficient of determination of 0.8. The Jarque-Bera test was implemented as well to test the null hypothesis for the normal distribution of standardised residual. Results support the null hypothesis with P-value = 0.222, which indicates the normal distribution of the standardised residual and its goodness. The standardised residual shows that the model can effectively predict solar radiation on a monthly basis.

关键词： solar radiation fuzzy neural nets normal distribution solar power regression analysis fuzzy reasoning time series mean square error methods power engineering computing solar radiation chaotic time series inputs time series adaptive neuro-fuzzy inference system Seoul city solar energy prediction model ANFIS normalised root mean square error coefficient of determination Jarque-Bera test null hypothesis normal distribution standardised residual P-value

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