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  • Abnormality Detection for Gas Insulated Switchgear

    using Self-Organizing Neural Networks

    Hiromi OGI, Hideo TANAKA, Yoshiakira AKIM OTO

    Tokyo Electric Power Company

    Computer & Communication Research Center

    1-4-10, Irifune, Chuo-ku, Tokyo 104 Japan

    Phone: +81-3-3501-8111 ext.5046

    Fax: +81-3-3297-1649

    Email: ogi@aisun.tepco.co.jp

    Keywords: Neural networks, Diagnosis, GIS

    Abstract: This paper presents an Artificial Neural Network

    (ANN) approach to diagnostic methods for abnormality detection

    for a Gas Insulated Switchgear (GIS). An outline of the current

    technologies of power equipment diagnosis is initially presented

    followed by the proposed application of the Self-Organizing

    Neural Network (SONN) to abnormality diagnosis of GIS. Several

    tentative experiments through laboratory simulations for a small

    sized GIS are finally presented.

    Introductjon

    Fault detection is an important task for the reliable operation of a

    power system. Recent survey of the diagnostic technologies and

    power equipment reliability [I] has shown that the majority of

    faults are caused by the maloperation of circuit breakers or

    switchgears. Gas Insulated Switchgear or Gas Insulated Circuit

    Breakers (GCBs) are being widely used to increase the reliability

    of power system operation. In order to maintain the high reliability

    of operation, it is necessary to detect in advance the abnormal

    operation of the GIS before its propagation to a major fault.

    Gas Insulated Switchgear or Gas Insulated Circuit Breakers are

    originally designed as maintenance free equipment. The main

    components are concealed in SF6 insulation gas and it is difficult

    to examine the internal status of operation. This invisibility has

    necessitated the development of the Predictive Maintenance

    Technology (PMT), concerned mainly with the examination of the

    internal status or abnormality of operation by the external

    application of sensors. One of the major drawbacks of PMT is the

    ineffectiveness of the currently available diagnostic algorithms in

    obtaining precise mathematical models to simulate the physical

    process of the internal abnormality.

    Artificial Neural Networks (ANNs), that mimic the nervous

    system, are finding wide applications as potential tools for the

    solution of problems where conventional approaches either fail to

    Yoshio IZUI

    Mitsubishi Electric Corporation

    Industrial Systems Laboratory

    8-1-1, Tsukaguchi-Honmachi, Amagasaki, Hyogo 661 Japan

    Phone: +81-6-497-7642

    Fax: +81-6-497-7727

    Email : izui@soc.sdl.melco.co.jp

    arrive at acceptable solutions or are provide unsatisfactory

    performance[2]. ANNs have been successfully applied to many

    problems in power systems and have promising applications to

    several other related areas[3-9]. The characteristics of ANNs such

    as learning, self organization, adaptation and non-linear

    classification provide important tasks of category formation and

    classification that are required to accomplish the diagnostic

    algorithms for PMT.

    The objective of this paper is to study Kohonen's Self-Organizing

    Neural Network (SONN)[ 10-11] for internal abnormality

    detection for GIS using sensor signal attached outside the tank.

    The neural network self-organizes its internal weights based on

    the probability distribution of spectrum of sensor signal. The label

    of abnormality category is assigned to each neuron after self-

    organization. This process is similar to the LVQ (Leaming Vector

    Quantization) algorithm. At the abnormality detection stage,

    unknown spectrum is classified as normal or abnormal status

    according to the label of the nearest neuron. That is, while

    category formation is conducted by unsupervised manner,

    classification criterion is conducted by supervised manner.

    In the next section, several diagnostic techniques for GIS are

    briefly reviewed. In the section three, a short introduction to

    Kohonen's SONN is given. Application of SONN to abnormality

    detection for GIS and experimental results are presented in the

    section thereafter.

    Diagnostic Technjques for GIS

    In this section, GIS is used as the abbreviation for gas insulated

    equipment. Investigation shows that breakdown due to insulation

    occupies a high probability of faults in the abnormal oper~tion of

    the GIS. The reason can be attributed to the fact that, while GIS

    using SF6 are designed to be compact with high insulation

    capability, small particles or mechanical un-adjustments while

    operation result in the gradual loss of dielectric strength and

    finally resulting in insulation breakdown[ I].

    1171

  • A recent report on the technology of insulation diagnosis of

    electlic power equipment in utilities has shown that more than

    40% of the current problems are accountable for the difficulty in

    the detection of abnmmal operation of GIS which are mainly due

    to the inability of assessing the internal status of a GIS[l2]. PMT

    plays an important role in alleviating this problem.

    The PMT detects small internal partial discharges, that provide

    signs of final insulation breakdown, with the help of sensors

    attached outside to tank. This is done so as to detect the

    abnollTlality at an early stage in order to avoid its development to

    a major fault. Abnormality detection using PMT can be divided

    into two major tasks, namely the development of sensors and the

    development of the diagnostic algorithm.

    Many developments have been reported for the detection of

    partial discharges using sensors[l 2]. These can be classified into

    the following major categories of I) Vibration or acoustics

    detection. 2) Electronic detection. 3) Optical or heat detection,

    and 4) Gas analysis detection. Examples of the first category are

    acceleration sensor, detecting the mechanical vibration of the tank

    and the ultrasonic sensor detecting the waves propagating inside

    the tank. Examples of the second category are the monitoring of

    the voltage for the detection of dielectric strength, and the use of

    electrode built in the insulation spacer. Examples of the third

    category are IRTV, detecting heat produced in the tank and

    photosensor method, detecting the radiation of light. The last

    categories to detect dissolved gases such as SF4, SOFz and HF

    caused by partial discharge. It can be concluded from the above

    examples that there exists several techniques for developing and

    selecting a suitable sensor for efficient detection of partial

    discharge.

    During the past few years, there has been few developments in the

    diagnostic algorithm and process using sensors. Most of the

    currently employed diagnostic algorithms use simple "threshold

    systems" . The basic principle of the threshold system is as

    follows . "If the output of the sensor signal is less than the

    predefined threshold, then the GIS system is of nollTlal status,

    else if the output is greater than the threshold, then the system is

    of abnollTlal status and the equipment needs to be investigated

    before the abnormality develops into a major disturbance".

    Even though the simple threshold system is easy to implement, it

    is associated with two major drawbacks. The first drawback deals

    with the difficulty in detection of the details of abnormality, such

    as the kinds of causes or location. The threshold system can only

    provide information between the normal and abnormal statuses of

    the equipment. The second drawback deals with the influence of

    the environmental noise on the response of the threshold system.

    It usually inferred that in most cases, if the output of the sensor

    signal is large then the GIS is in abnormal status. However the

    simple threshold system is capable of misclassifying the normal

    Topological Neighborhood

    .

    Label

    Neuron

    Input Data

    Fig. I A Self-Organizing Neural Network

    Neuron

    Category-1

    Category-4

    Feature Space

    Fig.2 Self-Organization and Learning Vector Quantization

    status in the presence of noise as that on the abnormal status. In

    order to overcome these drawbacks, detection techniques which

    are unaffected by environmental noise need to be employed for

    accurate and efficient classification. Self Organizing Neural

    Networks (SONN), which is described in the following section,

    promise to achieve the above mentioned tasks.

    Self-Organizing Neural Networks

    A brief review of SONN and Learning Vector Quantization (LVQ)

    is presented in this section. Kohonen's research for the

    development of SONN and LVQ has been motivated by the

    following experiment. In the experiment using cat visual systems,

    the fact was found that the order of arrangement of visual cell on

    retina is approximately same as the order of neuron excited by

    1172

  • corresponding visual cell on visual regions on the brain. This kind

    of correspondence between the arrangement of input and the

    arrangement of neuron is called topological mapping.

    Kohonen developed a solution to the above problem by expanding

    it to competitive learning. His idea was that the neuron

    topologicall

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