Analysis of Electrical and Non-electrical Causes of Variable Frequency Drive Failures

VFDs （variable frequency drives） are an integral part of many industrial plants and stations. Reliable operation and maintenance of these drives is vital to ensure sustained plant operation and availability. Understanding of the principles of operation of VFD systems as well as knowledge about their required operating environment is necessary for all operating personnel. Many times the operating personnel do not get involved with different technical issues until a complete failure has occurred. Hence, the awareness of the most dominant failure causes has a significant impact on assisting operators to avoid catastrophic failures and tremendous economic losses due to VFD shutdown. Proper plant design, accurate monitoring and data logging, following manufacturer preventive maintenance schedule, and choosing qualified team of operators can be the key to an efficient operation and a long lifetime for any VFD system. In this paper, we have analyzed the electrical and non-electrical causes of VFD failures based on a case study of a typical medium voltage VFD pumping station. Finally, recommendations are given from field analysis and observations.

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.

Aeroengine Fault Diagnosis Method Based on Optimized Supervised Kohonen Network

To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.

An Improved Biometric Fuzzy Signature with Timestamp of Blockchain Technology for Electrical Equipment Maintenance

The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Serial Failure Diagnosis of a Transmission Line Protection Relaying System by Petri Nets

With the development of large-scale complicated modern power systems, the requirement for the associated protection scheme tends to be more stringent and its combination more complex. However, it is very difficult to figure out the factors of failure of such systems. This paper proposes a Petri net model of a transmission line protection relaying system, including three types of relays as well as an automatic reclosing device, and shows how to diagnose serial failure of the system by analyzing invariant sets of the model. Furthermore, it gives four basic types of failure sequences and its execution is much more intuitive and effective than the traditional method.

Design and Realization of Distribution Network Intelligent Monitoring based on GSM Network

the power network fault detection system can only analyze all kinds of fault signal in stationary sequence： the internal grid and external disturbance presents degeneration, tiny, signal intensity changes randomly fluctuate, that will cause the system to detect the fault isolation ability of confusion and small fault signal is not strong; The article propose a method of power network fault detection for based on GSM, Through the underlying sensing equipment acquisition abnormal information of current, voltage power in the network and GSM networking scheme can filter the interference factors in extraction of fault information from and attribute value. The embedded gateway take STM32 chip as the core to monitoring data processing, to achieve a unified data management and user remote access, realizing method of system software are given, to construct the monitor management information platform. The actual test system show that, identification diagnosis ability of fault signal separation ability and small signal increases 17%, also meet the requirements.

Overview of Fault Diagnosis Theory and Method for Permanent Magnet Machine

Permanent magnet(PM)machines have been widely used in a variety of industrial and military applications due to their definite advantages of high power density and high efficiency.In some applications such as electric vehicles(EVs)and aircrafts,high reliability and security of the PM machine are critical.Hence,there is rapidly growing interest in strategies to improve the reliability and security of the PM machine from both academia and industry,where fault diagnosis is a requirement.In this paper,common faults of the PM machine are discussed,state of the art in fault diagnosis of PM machine are overviewed in detail,and different fault diagnosis methods are analyzed and compared.Finally,the development tendency of fault diagnosis techniques for the PM machine is prospected.