作者：刘士峰 郑麟 王一博
摘 要：随着经济的快速发展，电能成为人们生产和生活中最重要的一种能源，由于日常生活中所需电能的数量在不断的增加，电网因荷载量的增加对安全造成威胁，因此电网进行了大量的改扩建工程来保障正常的用电需求。断路器在电力系统运行中一直起着十分重要的作用，它可能有效的接通正常工作电流和快速的切断故障电流，从而使电力设备得到有效的控制和保护。因此在电力系统运行中，高压断路器的工作质量直接影响着电网的安全运行。本文对高压断路器的常见故障及原因进行了详细分析，从而为电力系统的正常运行提供了保障。 关键词：电力系统；高压断路器；故障原因
IEEE TRANSACTIONS ON POWER DELIVERY , VOL. 15, NO. 2, APRIL 2000585
Model-Aided Diagnosis—ANew Method for Online Condition Assessment of High
V oltage Circuit Breakers
Michael Stanek and Klaus Fröhlich, Senior Member, IEEE
Abstract—In recent years, online condition assessment of high voltage equipment is increasingly asked for by power utilities. However, most systems on the market today are merely monitoring solutions with hardly any diagnostic capabilities. This report reviews the basic principles for condition diagnosis from the field of artificial intelligence and introduces a novel strategy, named Model-Aided Diagnosis (MAiD).It was initially developed for condition assessment of high voltage circuit breakers but is also suitable for other technical devices. Theoretical considerations as well as practical results show that MAiD is a useful strategy for condition assessment. In addition, with its low hardware requirements it allows online condition diagnosis of high voltage circuit breakers locally by a low-cost monitoring device. Index Terms—Fault diagnosis, circuit breakers, modeling.
personnel in identifying a problem by analyzing the monitored data in much the same way a human expert would. This can help to reduce outage times of equipment or even prevent major failures by pre-warning of developing problems before they be-come serious.
This report presents a novel method for condition diagnosis of high voltage circuit breakers. It was developed with the prospect of implementation on a computer with low computational power in mind. Therefore, it can easily be incorporated into a dedicated local monitoring device or into an intelligent substation control system.
II. R EVIEW OF P RINCIPLES FOR C ONDITION D IAGNOSIS
I. I NTRODUCTION
URING the last decade, power utilities have displayed in-creasing interest in ways to extend the life-cycle time of their high voltage equipment. The most discussed approach is condition monitoring and diagnosis in order to find developing faults of the equipment at an early stage, or at least to identify the nature of a suddenly occurred problem from measured data. The findings of the second international enquiry on the reli-ability of high voltage circuit breakers by CIGRE working group 13.06indicate that almost half the major and minor faults occurring are located in the operating mechanism. Hence, the greatest emphasis on condition monitoring and diagnosis should be placed on this component. This recommendation was also followed in the present work.
During recent years, several devices for condition assessment of high voltage circuit breakers have been introduced (seee.g. )by circuit breaker manufacturers and also by independent companies. However, close examination of these devices reveals that these are plain monitoring solutions i.e. they record sensor data and perform boundary checks and similar operations on them. In most cases, the operator is left to drawing his own con-clusions on the cause and nature of the fault, based upon the numbers presented by the monitoring device.
In the field of artificial intelligence (AI),much effort has been devoted to condition diagnosis ,which may also be applied to high voltage circuit breakers. Its purpose is to assist substation
Manuscript received January 26, 1999. This work was supported by ABB High V oltage Technologies Ltd., Switzerland, and ABB Power T&DCompany Inc., Greensburg PA.
The authors are with the High Voltage Laboratory, Swiss Federal Institute of Technology Zurich, 8092Zurich, Switzerland.
Publisher Item Identifier S 0885-8977(00)03476-2.
As defined in ,“diagnosisis the process of determining the nature of a fault or malfunction, based on a set of symp-toms. Input data (thesymptoms) are interpreted and the under-lying cause of these symptoms is the output.”In practical ap-plication on a high voltage circuit breaker it is to identify the faulted component(s)and the kind of fault in the breaker from the data provided by its sensors.—Notethat in this context the word “sensor”is used in a broad sense to include such elements as voltage or current transformers and limit switches.
Three basic strategies for assessing the condition of a system under observation are known, namely •rule-based diagnosis, •model-based diagnosis, •case-based diagnosis.
Since they are the basis for the decision to develop a new approach they will be introduced briefly with their respective strengths and shortcomings. For details the interested reader is referred to literature on expert systems and artificial intelli-gence .
A. Rule-Based Diagnosis
Rule-based diagnosis is based on a set of heuristic relation-ships between symptoms and causes which form the knowledge base for the diagnostic process. These relationships are called rules. In condition assessment, a diagnosis is found when all rules pertaining to it are fulfilled.
Strengths:The knowledge base can be entered fairly straightforward by a human expert to reflect his or her ex-perience. It is very efficient in terms of required computer performance and computation time. Small changes to the knowledge base can be implemented fairly easily.
586Shortcomings:Rules are mostly heuristic in nature (“shallowknowledge”)and lack a deeper understanding of the principles governing the operation of the system. A human expert needs to design the rules, which is a tedious process and carries the possibility of introducing errors into the knowledge base. It is almost impossible to handle missing or unexpected data as well as unforeseen problems. The method is not flexible in creating a knowledge base for a new or enhanced system (differenttype of circuit breaker) from an existing one. In many cases, diagnosing faulty sensors requires a large additional number of rules, increasing the complexity of the knowledge base.
B. Model-Based Diagnosis
Model-based diagnosis uses a computer model which con-tains the structure as well as the parameters of the device, im-plicitly comprising the physical principles which govern its op-eration and possible malfunctions. The diagnostic engine runs the model base and automatically finds all diagnoses that can explain or at least are consistent with the observed anomaly. Strengths:The model contains “deep”knowledge i.e. it is based on the laws of physics governing the operation of the de-vice. Simulation permits access to “hidden”quantities (notac-cessible for direct measurement). The model base can be modi-fied for use on a different device fairly easily. No previous field experience is necessary, making this a suitable technique for novel devices. Previously unforeseen or multiple faults can be detected provided they can be explained through the model. A faulty sensor can be detected as easily as a fault in the system itself.
Shortcomings:The initial effort for creating and adjusting the model is substantial. Computing the diagnosis requires high computer power. Structural changes in the device (e.g.a bridge fault between two adjacent wires) are almost impossible to identify.
C. Case-Based Diagnosis
Case-based diagnosis uses an explicit database of past prob-lems with their symptoms in order to find a solution for a new problem situation. As opposed to the rule-based and model-based approaches, the knowledge base for case-based diagnosis does not need to be assembled explicitly by a human expert, rather it is generated “automatically”by analyzing actual cases from the past and generalizing them for future use. A diagnosis is found by retrieving the best matching case from the knowl-edge base, modifying it if necessary, and applying it to the new problem situation. The result can be stored as a new case for fu-ture use.
Strengths:The knowledge base is generated automatically, reducing the overhead and error probability of implementation. The system “learns”with every incident. The knowledge base is always consistent and correct. A faulty sensor can be detected as easily as a fault in the system itself.
Shortcomings:Since a history of fault cases is required to fill the knowledge base, this approach is hardly suitable for novel systems. Cases contain no “deep”knowledge. The initial effort for designing the system (storing,retrieving, and matching of
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 2, APRIL 2000
cases) is still substantial, so far no universally applicable auto-mated methods have been proposed.
D. Application to High V oltage Circuit Breakers—Today’sSituation
Following the evaluation of the three basic diagnosis strate-gies listed above it was determined that the model-based ap-proach is the best suitable one for condition diagnosis of high voltage circuit breakers. However, a market study revealed that the presently available software packages for model-based di-agnosis are not suitable for this purpose, for several reasons:System requirements:The model-based systems under con-sideration require a UNIX workstation to run, one for each cir-cuit breaker.
Price:In addition to the necessary computer platform the model-based diagnosis software must be purchased, one license for each installation, which is often more than a thousand US$for a single run-time version.
Data Transmission:For a centralized workstation-based monitoring system as described above, all the sensor data need to be transmitted to the substation computer for evalu-ation. However, substations are not generally furnished with high-speed data links between switchgear and the station house, the standard is very often still conventional wiring. Hence, it would be more useful to perform the monitoring and diagnosis task locally at the breaker, by a specialized device, and only report problems to the substation personnel. The previous arguments make it clear, though, that this is not a feasible solution with the systems available on the market today.
Therefore, the remaining options were either to use a strategy different from the model-based one or to find a new approach to using it. The latter way was chosen and the result is described in the next section.
III. M ODEL -A IDED D IAGNOSIS AS A N EW M ETHOD
C ONDITION A SSESSMENT
The approach for diagnosis introduced in this report is a novel combination of the case-based and model-based diag-nosis strategies, in order to retain certain important advantages of both. This is achieved by evaluating the model off-line line to simulate the anticipated fault modes of the circuit breaker. A search module, which can be rather simple, uses the resulting fault cases to find the most likely diagnosis. This approach is called Model-Aided Diagnosis (MAiD);it is described in detail below.
A. Basic Concept
Model-aided diagnosis is based on a functional computer model of the system under observation. This model can accurately simulate the system’sbehavior under normal and fault conditions. When it is fed with the internal and external operating conditions it will yield the same data that would be measured by the sensors under these conditions.
From the sensor data, meaningful quantities for assessing the condition of the system are extracted. These are called features. The set of all features at a given time is called system status. In addition, the confidence of the diagnosis can be
STANEK AND FRÖHLICH:MODEL-AIDED DIAGNOSIS
Fig. 1. Algorithm for Model-Aided Diagnosis.
increased by compensating for dependencies of the features on environmental influences such as temperature prior to further processing.
The basic strategy for MAiD is two-fold. First the computer model is used to simulate all possible faults; the results are stored in a database. This database is used on-line for finding the most likely diagnosis when a deviation from normal opera-tion has been detected. Fig. 1gives the algorithm of MAiD in form of a structogram. For details the interested reader is re-ferred to ,where each step is covered in depth. B. Discussion
MAiD is a powerful combination of the model-based and case-based approaches to condition diagnosis. Originally devel-oped for diagnosing high voltage circuit breakers, it can be used for condition assessment of most kinds of technical equipment, as long as a model of the device exists, with moderate expendi-ture.
Part I (Preparation)of MAiD is essentially the model-based part. The computer model is used to conduct “experiments”by simulating various operating conditions. The results are used to build a knowledge base for the case-based second part (Diag-nosis), where the best matching case is looked up to produce a diagnosis for the current condition.
Strengths:Much like true model-based diagnosis, MAiD permits access to quantities that are not or cannot be measured in the system, helping to really pinpoint the location and kind of the problem.
MAiD can be used for novel types of equipment (HVCB’s)even before any operational experience has been gained since all conceivable faults can be simulated beforehand. Thus, a pow-erful system for condition monitoring and diagnosis can be sup-plied with the device from its first installation.
Since the actual diagnosis (Part2of the algorithm) requires no more than finding the best matching case in a pre-defined database, it can be executed on a low-performance computer or monitoring device. Or, for even lower computing demands on the peripheral hardware, only the feature extraction can be performed locally and the actual diagnosis is performed by a central diagnosis computer which holds the databases of several pieces of equipment.
Adaptation to similar types of equipment can be performed fairly easily by modifying the model operating parameters to suit the new application. After that, all that needs to be done is generating a new database and installing it onto the diagnostic computer. Since the database is generated automatically, the in-formation contained therein is always consistent and complete. If a fault not originally considered is encountered later it can easily be added to the database if desired and if the diagnosis software permits this option. In order to maintain consistency with other installations, though, the recommended procedure is to incorporate this fault into the fault list, generate a new data-base, and install it on the diagnosis system.
Shortcomings:MAiD works best for analysis of transient events; for continuous supervision, it is necessary to define events for conducting the diagnosis, either periodically or from certain changes in system operation. This is due to the fact that simulation of the operating condition needs to be conducted be-forehand, and the simulation can only run for a finite duration. However, careful pre-and post-processing of the sensor and simulation data as well as selection of the features for diagnosis can to a large extent mitigate this limitation.
If the occurrence of more than one fault at the same time shall be taken into account the database can grow very large quickly, increasing the storage requirements on the diagnostic system. In calculating the necessary memory size it must be considered that the number of simulated cases N is
N =E 1S;
E S ... ... number number of of elements operating in states the list e.g. from closing Part or I Step opening 1, the
breaker, CB on-line or not, etc.
IV. A PPLICATION TO H IGH V OLTAGE C IRCUIT B REAKERS As mentioned before, the initial application for model-aided diagnosis is on high voltage circuit breakers. A live tank breaker with spring-hydraulic mechanism was selected for a first test implementation, for two reasons:First, it is a well-established product with thousands of installations worldwide. Second, one single-phase exemplar of this breaker type, was available to the author for functional analysis and measurements. A. Computer Model
The circuit breaker employed is an SF ac circuit breaker with two 6-insulated self-blast high voltage interrupters on one
588IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 2, APRIL
Fig. 2. Simulink model of high voltage circuit breaker. PT =Potential Transformer, CT =Current Transformer, R =resistance. Other abbreviations on port names refer to the origin or destination block of the signal, e.g. TC =Trip Coil.
column, in T shape, per phase. It is operated by an hydraulic mechanical drive where the mechanical energy for the opera-tions is provided by a plate spring assembly. Energization of a control coil actuates a change-over valve which in turn applies or removes the system pressure to/fromthe main piston head, causing it to move from OPEN to CLOSED position or vice versa. The oil volume lost from the high pressure storage is re-placed by a hydraulic pump, which is controlled by the position of the spring column via a limit switch.
These functions were implemented in Simulink (atool for dynamic system simulation for M ATLAB ) to form the circuit breaker model. All components were modeled individually and finally assembled into the complete system. Since—accordingto the CIGRE study —thedrive mechanism is the most fault-prone part of an HVCB, the highest effort went into modeling this component with all its relevant elements.
The complete model is shown in Fig. 2. No detailed descrip-tion can be given here, rather its purpose is to give the reader an impression of the complexity involved.
This model can simulate the function of the circuit breaker and each component, which is the important part for diagnosis, rather than the physical structure. It is called and evaluated by means of several M ATLAB functions. B. Test Implementation
For simulating various faults and operating conditions in the circuit breaker, a number of parameters can be changed in the computer model. When changed from their default values, in
D IAGNOSTIC F EATURES FOR T EST I
most cases this results in a “fault;”the diagnosis attempts to find out which input parameter was changed in what manner. The diagnostic features were selected such that even without special training they would be meaningful to operating
STANEK AND FRÖHLICH:MODEL-AIDED DIAGNOSIS
Fig. 3. Diagnosis report from test implementation, with reduced hydraulic flow cross-section (cf.Fig. 5, curves b). The default and estimated values in the upper section of the window refer to the parameter which caused the fault. The lower section contains a complete list of features (scrollable)with their normal and measured values and the corresponding values of the best matching case from the
Fig. 4. Excerpt from circuit breaker model, containing those components and quantities which directly influence the piston position.
personnel. The specific list of features used in this test im-plementation is presented in Table I. They can be derived easily from the measured quantities. In addition, most of the required sensors are standard equipment of new circuit breaker monitoring devices or even the circuit breakers them selves. A simple user interface was also created in M ATLAB , which presents the most likely diagnosis together with a confidence value contains list of features (Fig.3). This list contains the default, measured and estimated values of each feature, thus making the diagnostic process more transparent to the
Fig. 5. Simulated (dottedlines) and measured (solidlines) travel curves for circuit breaker opening; (a)no fault, (b)reduced hydraulic flow cross-section.
The process of condition assessment by model-aided diag-nosis shall be illustrated by an example. Consider the model segment depicted in Fig. 4, which generates the position of the main piston at simulation time. Parameters directly influencing its operation are, among others, the initial position and the hy-draulic damping.
590During the preparation part of MAiD, simulations were con-ducted for both closing and opening the circuit breaker, with three different values for the damping parameter, which has a default value of 1. Simulation of a normal opening operation from initial position =1(fullyclosed) resulted in the travel curve shown by the dotted line (a)in Fig. 5. From this curve, the fea-tures contained in the “Normal”column in the user interface (Fig.3) were extracted. Another simulation with the hydraulic damping parameter set to 5produced the dotted line (b)with the features contained in the “Matched”column. Both sets of fea-tures were stored in the database.
When the real circuit breaker was operated in normal condi-tion, a travel curve according to the solid line (a)in Fig. 5was recorded. Its features are rather close to those from the no-fault simulation. Then the hydraulic flow cross-section for opening was reduced by an arbitrary amount. The travel curve from the next opening operation is shown by the solid line (b);its features are shown in the “Measured”column. Calculating the distance from all stored simulated cases, the best match was found to be the one discussed above (hydraulicdamping =5). Hence, the according diagnosis was reported to the user. D. Practical Results
The parameters of the computer model were manually ad-justed to match the particular circuit breaker under observation. With that, the diagnostic database was created and used on the high voltage circuit breaker described above.
The following quantities were measured on the circuit breaker:close and trip coil energization (beforethe auxiliary contacts), close and trip coil voltage (afterthe auxiliary con-tacts), close and trip coil current, linkage travel, motor voltage, motor current. These quantities were recorded by means of a PC-based monitoring system and evaluated off-line at a later time. No spring travel sensor was available, hence the spring position after each operation was estimated and manually entered by the operator. It is believed that monitoring this quantity directly would have further increased the diagnostic confidence.
Several abnormal operating conditions were applied to the circuit breaker, namely,
1) the hydraulic flow cross-section was reduced, causing a low opening speed (Fig.5);
2) a differently aligned pump motor switch was used for con-trolling the motor, resulting in low spring charge;
3) the control voltage for opening or closing the breaker was changed from 110to 80V or 130V , respectively.
At first, one change was made at a time; later, up to three changes were applied simultaneously. In every case, the ab-normal condition (orone of them in case of multiple faults) was correctly identified from the measured data. Obviously, the con-fidence values were higher for the single fault cases than for the multiple faults. In addition, the confidence was further increased by quantizing the simulation results to match the resolution of the measured data.
Fig. 3shows the diagnosis for the fault case of reduced hy-draulic flow cross-section (Fig.5). The first row of fields in the
IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 15, NO. 2, APRIL 2000
upper half of the window gives the circuit breaker operation (open)and the connection status (disconnectedfrom the net-work). The remaining fields are the diagnosis results. Here it has been found that the hydraulic damping of the main piston was increased from its default value of 1to approximately 5, with a confidence or 35.4%.With the scrollable features list in the lower part of the window, the user is enabled to follow the reasoning that led to this diagnostic decision.
V . C ONCLUSIONS AND O UTLOOK
Model-aided diagnosis—thenovel strategy for condition di-agnosis introduced in this report—isa useful method for con-dition assessment of technical devices. Its feasibility has been demonstrated on a real high voltage circuit breaker.
The benefits for the user of model-aided diagnosis include, •Determining the location and cause of a problem in a technical system, possibly even before it becomes serious. Thus the time for identifying the problem and with it the outage time can be reduced. This also helps to reduce the costs for operation and maintenance.
•Access to quantities which are not accessible for direct measurement, inside the system. This provides more de tailed insight into the overall condition of the equipment. •Since model-aided diagnosis does not require high com-puting power in each installation it can easily be integrated into new or existing monitoring or EMS hardware. In this manner, the capabilities of a condition monitoring system can be enhanced significantly at hardly any increase in cost per unit.
Further work on the concept of model-aided diagnosis should include:self-adaptation of the model parameters to the specific device to be diagnosed; finding the best methods for pre-and post-processing of the sensor data; incorporation of trend anal-ysis and extrapolation with diagnosis; interfacing a model-aided diagnosis system with intelligent EMS.
The authors would especially like to thank Mr. A.C. Carvalho for his support and valuable discussions.
A.L.J. Janssen et al. , “ASummary of the Final Results and Conclusions
of the Second International Enquiry on the Reliability of High V oltage Circuit Breakers,”in CIGRE 1994session , 1994, paper no. 13-202. J. Reason, “Comingsoon! Circuit breakers with on-line condition moni-toring,”McGraw-Hill’sElectrical World , vol. 209, no. 2, February 1995. G.F. Luger and W.A. Stubblefield, Artificial Intelligence:Structures and
Strategies for Complex Problem Solving , 3rd ed. Harlow:Addison Wesley Longman, 1998.
A.A. Hopgood, Knowledge-Based Systems for Engineers and Scien-tists . Boca Raton:CRC Press, 1993.
M. Stanek, M. Morari, and K. Fröhlich,“Model-AidedDiagnosis:An In-expensive Combination of Model-Based and Case-Based Condition As-sessment,”IEEE Transactions on Systems, Man and Cybernetics, 1999, submitted for publication.
STANEK AND FRÖHLICH:MODEL-AIDED DIAGNOSIS
Michael Stanek was born in Vienna, Austria, in 1965. He studied electrical engineering at the Vienna University of Technology, Austria, where he ob-tained his Dipl.-Ing. degree in 1993. Presently he is studying for his Ph.D. at the High Voltage Laboratory of the Swiss Federal Institute of Technology Zurich. His research activities are focused on the intelligent high voltage circuit breaker, in particular controlled switching and on-line condition monitoring and diagnosis. In 1998, Michael Stanek received the Innovation Award of the Swiss Electrotechnical
Association for his work on a controlled switching device for high voltage circuit
breakers. Klaus Fröhlichwas born in 1945in Salzburg, Aus-tria. He received a Master of Electrical Engineering and a Ph.D. degree in technical science from the Vienna University of Technology, Austria. After 11years in Switchgear and High V oltage Technology with BBC (laterABB) in Switzerland he became a full professor at the Vienna University of Technology in 1990. Since 1997he has been a full professor of High Voltage Technology at the Swiss Federal Institute of Technology Zurich, Switzerland. Klaus Fröhlichis a senior member of IEEE, a member of
CIGRE Study Committee 13, and the convenor of CIGRE Working Group 13.07(ControlledSwitching).
广安发电有限责任公司，四川广安638017 张仕宁 阅读次数：3
摘要：分析了高压真空断路器故障原因及处理情况，目前断电器运行良好。关键词：高压真空断路器；故障；分析；处理；运行情况中图法分类号：TM561.2文献标识码：B 文章编号：1003－6954(2002)增－0036－02广安发电有限责任公司厂用高压6kV 断路器采用的是天水长城高压断路器厂生产的ZN28－10型真空断路器，共安装了90余台。自1999年10月电厂投产至今，经过两年多的运行和维修，此种断路器暴露出一些问题和不足，根据长期运行和维修情况，对ZN28－10型真空断路器进行分析并阐述一些自己的认识。1 ZN28－10型真空断路器的基本结构及主要技术参数(1)ZN28－10型真空断路器采用ZMD11－10系列玻璃外壳，中封式纵向磁场触头真空灭弧室并配用CT19型弹簧操作机构，该厂(下同) 采用直流110V 作操作电源。操作机构和真空灭弧室采用前后布置。ZN28－10型真空断路器为手车式，操作机构通过大轴、绝缘拉杆与真空灭弧室的动导电杆相连接，带动真空灭弧室的动触头沿着导向装置以规定的机械参数作分、合运动。(2)ZN28－10型真空断路器的主要技术参数ZN28－10型真空断路器的主要技术参数见表1。
2 ZN28－10型真空断路器在使用中发生的故障分析及处理对策(1)真空断路器插头与母线插刀之间的距离是断路器运行是否良好的关键。2000年上半年在6kV Ⅰ段做断路器预防性试验的时候，发现ⅠA 段工作电源进线(63103)断路器的上端头三相一次插头的插入深度(见图1) 太少，只有10～15mm ，不符合断路器的有关规定，给安全运行造成了隐患，但是，此种型号(3150A)的断路器一次插头没有调整功能。对这种情况进行了认真的分析后，决定在断路器的三相一次插头与断路器本体接头之间加装一块厚10mm 表面搪锡的铝板，这样使插头的插入深度满足要求。到目前为止这台断路器已安全运行达一年时间。
(2)经过两年左右时间的运行，该厂6kV 真空断路器有一半以上出现了合闸半轴的扣板边缘被磨成圆角，造成扣接参数达不到要求，有可能引起断路器在进、出车或异常震动时发生自动合闸事故。分析原因：①由于断路器的扣板刚度不够，在频繁分、合的情况下，容易产生磨损；②断路器合闸半轴与扣板配合的参数多为下限，要求应为1.8mm ～2.5mm ，目前大多为1.8mm 。出现问题后，对各台断路器进行重新调整，使断路器的合闸半轴与扣板的配合参数调整为上限2.5mm ，目前断路器运行良好。如果发现扣板边缘磨损过度，应更换高强度材料的扣板。
(3)在2000年上半年6kV Ⅰ段及2001年6月6kV Ⅱ段做断路器预防性试验的时候，都发现各有一台断路器的一相拐臂与水平拉杆之间的销子退出将近一半，如果销子全部退出，将造成断路器非全相合、分的严重事故，就会引起全段母线失电。分析原因：是销子的挡卡在运行过程中松动脱落，从而造成销子退出，以至引发事故。采取措施：①运行及维护人员应加强对断路器销子的检查，发现有退出现象，及时调整；②将断路器的挡卡销子更改为不易脱落的开口销子。(4)2001年该厂发生了一起带接地刀闸合断路器的事故，通过认真检查和分析，该厂6kV 真空断路器的“五防”措施中防止带地刀合断路器的措施存在缺陷。其地刀合上后挡住小车进入柜内的挡块与小车的配合尺寸太小(约10mm) ，而且地刀合上后，地刀的指示、定位挡块大多卡涩，不易弹出到定位孔内。经过研究，在6kV Ⅰ、Ⅱ段断路器柜内的地刀挡块上焊接了一根18mm 的圆钢，比原挡块长15～30mm ，这样增加了地刀与断路器的配合尺寸，并对地刀的指示、定位挡块进行重新调整、加油处理。使地刀指示、定位挡块均能轻松、顺利地弹回定位孔内，从而解决了这一缺陷。3 新技术、新工艺在高压断路器上的应用前景在科技信息日新月异的今天，有关高压断路器的新技术、新产品层出不穷。以前，对真空断路器的真空度无法进行定量的检测，只能通过对真空灭弧室进行交流耐压测试来进行定性地判断真空灭弧室是否漏气，现在已研制出了检测灭弧室真空度的新型仪器，可以对真空断路器灭弧室的真空度进行定量分析。如今，红外线成像技术在高压断路器上的应用越来越广泛，它可以对高压断路器进行在线检测，对断路器的各个部件的发热程度随时进行检测，从而及时提供断路器的运行情况，及早发现事故隐患。为了保证该厂安全发、供电，建议尽早采用新技术、新产品。