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Conference Proceedings Proceedings of Conference of ISEIM ISEIM 2017 2017
A Review On Condition Monitoring Of GIS Animesh Sahoo, Aravinth Subramaniam*, Saurabh Bhandari and Sanjib Kumar Panda, Sembcorp-NUS Corporate Laboratory, Department of Electrical and Computer Engineering Faculty of Engineering, National University of Singapore, Singapore 117580 *Email:
[email protected]
Abstract—Baseload power generation plants like thermal, coal, nuclear etc. contribute to around 30-35 % of the total power demand. Electric power grids depend on these power plants to provide reliable electric power supply with good power quality to the end consumer. To ensure reliable power generation, critical components of these plants need to be monitored continuously. In the distribution side of power system networks, medium voltage switchgear is being considered as the vital component to be looked at for industrial applications. In the past research studies on MV switchgear have given enough insight into various kinds of failures associated with it. This paper focuses on Gas insulted Switchgear (GIS) failure causes, failure phenomena, sensing techniques and signal processing tools for online diagnosis. Keywords—HV, Condition monitoring, GIS, PD, Fault Diagnosis.
I. INTRODUCTION High voltage switchgear and equipment for voltages ranging from 1 kV to 800 kV are considered as safety elements within the electrical energy supply and are therefore requires very high standard of availability and reliability. Gas insulated switchgear is used in industrial areas to meet the requirements of high energy demand by space saving design with minimum cost. The high voltage GIS have been extensively used worldwide for more than 40 years ensuring reliable operations [1]. When GIS operates on long term it suffers various kinds of faults which lead to have longer maintenance time, major economic loss and interruption of stable power system operation [2]. The faults associated with GIS can be detected by various methods such as electrical, ultrasonic, chemical as well as optical [3], [4]. GIS is a closed electrical equipment sometimes it becomes difficult to trace the fault at the early stage depending on the human senses [5]. As GIS takes minimum space and all its components are placed very close to each other, the occurrence of fault in any one part will easily affect the other parts and which lead to the expansion of faults [6], [7], [8]. So preventive maintenance for GIS is a must to detect the failure at its incipient stage. Circuit breaker has two important features as it provides reliable maintenance and current interruption. It can be used both in medium as well as high voltage switch gear in the future [9], [10]. As per the data provided by International Council of Large Electrical System (ICLES) in the year The research is supported by the National Research Foundation Singapore, Sembcorp Industries Ltd and National University of Singapore under the Sembcorp-NUS Corporate Laboratory.
2004 the onsite CB failures are mainly associated with their operating mechanism. One of the fundamental problem of GIS is electrical induced stress on the equipment insulation level under Very Fast Transients caused by switching operation [11]. SF6 gas is used in GIS which is having high chemical inertness but on continuous electrical stress it decomposes and reacts with H2 O and O2 to form impurities of SF6 which become the consequence for several abnormal discharges in GIS. The chemical by-products in the impurities are SOF4 , SOF2 , SO2 F2 , SF4 , SO2 , CF4 , CO2 , HF, etc. [12]. Analysis of these by-products has become an established offsite condition monitoring technique for GIS [13]. The non-linear VI characteristics of Metal Oxide (MO) surge arrester resistors enable them to protect GIS during over voltages caused by lighting strokes, earth faults or regular switching actions [14]. The CESI laboratories have researched thoroughly about the response time of MO resistor blocks used in surge arrester to very steep front impulse voltages by means of co-axial generating and measuring circuits [15]. II. LITERATURE BASED REVIEW AND DISCUSSIONS A. GIS FAILURE CAUSES The various components of the GIS can be classified based on: • Power Conducting Components that conduct or interrupt the flow of electric power such as Switches, CBs, fuses, Surge Arresters etc.; and • Control systems that monitor and control the power conducting components such as control panels, current transformers, potential transformers, protective relays and associated circuitry. The schematic in terms of single line diagram for GIS is shown in Fig. 1.(a) and its internal layout in Fig. 1.(b) [16]. However, when a fault occurs the power conducting components undergo a severe damage compared to the control system as they carry the fault current through them. Therefore, it is important to understand their failure causes which are briefly discussed below. During the design of GIS bus bar, the main parameters have to be looked at are insulation level and current carrying capacity of the main conductor. Again, the current carrying capacity mainly depends on the operating temperature. Thermal failure generally occurs in case of GIS bus bar when the
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Figure. 1: (a). Typical GIS single line diagram and (b). its internal layout
operating temperature exceeds the nominal temperature [17]. The abnormal temperature rise of the bus bar is the result of either Joules heat due to fault current flowing in the main conductor or excess heat generated due to eddy current flow. This heat is generally dissipated to the environment by the process of radiation as well as natural convection. By this generated heat, other functional parts of the GIS are also get affected. Besides this thermal failure now a days mechanical failure of the GIS is becoming main concern. The main cause for the mechanical failure is the GIS bus-bar vibration. The periodic current signal produces an electro-dynamic force and this force causes the bus bar to vibrate. These vibrations on continuous operation many times lead to loosening of the bolts, gas leakage, pressure drop and insulation failure like consequences. The continuous ageing process many times lead to several kinds of discharges like PD, Corona discharge as well as surface discharge and these phenomena serve as the signature for future catastrophic failure such as failure of CB auxiliary and control circuitry. In case of GIS sometimes faulty SF6 monitoring system acts as an indicator to have impact on dielectric rigidity as well as high fault current interrupting capability of CB. The failure rate of the GIS surge arrester is quite low (around 0.1% per year) compared to the failure rate of other active part of GIS [18]. The main causes contribute to the failure of GIS surge arrester are the failure of MO resisters, different insulating parts like FRP-rods, plates, partitions and metal enclosures.
B. VARIOUS PHENOMENA ASSOCIATED WITH GIS FAILURE Based on the types of faults associated with various components of the GIS as discussed above it can be concluded that all the faults contain electrical , mechanical ,thermal and chemical signatures associated with different types of phenomena as a precursor of the fault. Kay et al. has surveyed all these phenomena has been summarized in the Table I [19]. C. AVAILABLE SENSORS TO DETECT THE FAILURE Once the fault signatures according to different failure mechanisms of the GIS are being generated several types of detecting sensors of different sensitivity as well as frequency bands installed or mantled can be utilized on various part of the GIS to capture those faulty signals. It should be of main concern to have a survey about those detectors properly before keep them in operation. Table II summarizes various kinds of sensors available in the present condition-monitoring scenario for GIS. D. USE OF MODERN DATA ANALYTICS FOR ANALYSING THE SENSOR ACQUIRED DATA. 1) FFT: It has been considered as one of the vital signal analysis tools for spectral decomposition and analysis of the fault signal captured from various sensors. It computes the discrete Fourier transform (DFT) of a sequence, or its inverse. Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. It does not contain any time information of the signal. 2) DWT: This is being a most powerful signal analysis tool that extracts features of the signal in time as well as frequency domain. It has a movable window size, which is wide for high frequencies and narrow for low
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Conference Proceedings of ISEIM 2017 TABLE I. Name of the Phenomena Electromagnetic Acoustic Optical HFC components Harmonic current components Thermal)
Chemical
Details of the Phenomena 1. Current flowing through loose connections. 2. SD and Corona initiated discharge causes electromagnetic signal radiation. 3. Frequencies in the range of,HF,VHF and UHF 1. Mechanical Vibration within the Electrical equipment. 2. Immune to EM noise. 3. Frequency band of 10-300 KHz. 1. Various Ionization, Excitation and Recombination process. 2. Intensity and Wavelength depend on Insulation material, temperature, PD intensity and Pressure 3. PD mainly lies on UV,Visible and IR regions 1. PD based surge of electrons Contain HF., 2. Superimposed with Normal Current 1. Basically deals with 3rd, 5th,and 7th harmonics present in the fault current., 2. Not got significant acceptance. 1. Mainly effective with LV power equipment., 2. Ionization of Metals occur when subjected to elevated temperature., 3. Ions are produced in gases faster than metals under high temperature. 1.Common by-products during PD occurring in GIS are O3 and N2 O., 2. Forms HN O3 when reacts with moisture and water molecules which is very dangerous for dielectrics and insulators in SWG.
TABLE II. Name of the Detector RF Antenna
Coupling capacitor Capacitive sensor HFCT Rogowski coil Piezoelectric Ultrasonic Sensor UV Sensor Thermal Sensor D-Dot Sensor
PHENOMENA ASSOCIATED WITH GIS FAILURE CAUSES
SENSOR DETAILS USED IN CONDITION MONITORING OF GIS
Features 1.Converts EM signals to electric signals., 2. Detects PD in HF/VHF/UHF range. 3. Different types of antenna are loop bi-conical, log-periodic and stick antennas. 1. Transforms PD energy from source to measurement set up., 2. Used as proximity sensors V and I measurements., 3. Most popular sensor is Epoxy-Mica-Encapsulated capacitive couplers., 4. Commercially available are 80pF-2nF.,,Design constrains are frequency and inductance. 1. Stray capacitance between the high-voltage parts within the equipment and the electrode of the CS works as coupling capacitor. 1. Magnetic field around a wire caused by HF current which induces a voltage in the windings of HFCT., 2. Inductive sensor., 3. Can detect PD in the range of several hundred megahertz. 1. Operates on Faradays law of EMI., 2. Inductive Sensor. 3. Detects PD in the range 1-4 MHz. 1. Rely on Piezoelectric effect created by PD. 2. Directional sensors., 3.Operates in 100-130 kHz. 1. Electrons emitted from the material or the change in electrical resistance can be detected by this sensor., 2. Sensitive to UV light only., 3.UV spectrum is 10-400 nm. 1. Less sensitive., 2. Large number of sensors required for complete Switchgear protection., 3. Conventional thermal IR sensors and thermographs used to detect hotspot due to PD. 1. Co-axial wide band sensor., 2. Measures the electric displacement density and hence the name., 3. Can be installed into the wall of a switchgear compartment.
frequencies. The Discrete Wavelet Transform (DWT) of a signal can be formulated by a series of low pass filter and high pass filter and then decompose the original signal into high pass (details) and low pass (average) coefficients. 3) RPA: Graphical representation based information can be extracted from time domain representation by using a tool known as Recurrence Plot Analysis(RPA). In mdimensional phase space, it counts the degree of aperiodicity of time series. This tool maps the non-linear characteristics of the acquired signal by the use of visible rectangular blocks, which are having higher density of points [20]. The traced signal is assumed stationary in
the given period provided its texture patterns within the rectangular block of RP mapping is homogeneous. The parameters need to be determined to quantize a signal in a RP are recurrence rate (RR) and determinism (DET).The value of RR signifies as how much the vectors are close to each other in the phased space. On the other hand, from DET differentiates the recurrence points in the diagonal direction from the isolated recurrence points in RP map. Generally the investigated system assumes a less deterministic ingredient if it yields lower value of DET. 4) ANN: Artificial Neural Network(ANN) is a non-linear machine learning tool of Neural Network used to recognize the pattern and predict the time series of a set
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Conference Proceedings of ISEIM 2017 of raw data. It also helps in clustering them. When all the sensor acquired data captured during online condition monitoring process are given to this tool; mathematically it processes them as input data, multiplies them with weighted values and then sums them together [21]. A user defined non-linear transfer function is used to train the output data against the target values. The mismatching between the test data and trained data is given back to the input layer as error. The weighted values get updated every time following those errors. Through this iterative approach ANN learns the non-linear relationship between the input and output data. 5) SVM: It has mainly two classes of applications, classif ication and regression. The classification problem can be restricted to consideration of the two-class problem without loss of generality. Masume Khodsuz et al. have used a multi-layer Support Vector Machine(SVM), which is a machine learning method based on Statistical Learning Theory(SLT), classify MOSAs operating conditions based on extracted features of experimental tests and he has proved that the classifier has an excellent performance on training speed and reliability which confirm the high applicability of introduced features for correct diagnosis of surge arresters conditions [22]. 6) HHT: Hilbert Huang Transform(HHT) generally decomposes a non-linear and non-stationary signal into nempirical Intrinsic Mode Function(IMF) modes and a residue which can be a mean trend or a constant. It can be used as a signal analysis tool in the field of GIS condition monitoring as it distinguishes the faulty signal from the healthy one by the process of signal classification, pattern recognition as well as feature extraction process. The deduced IMFs by this transform are different for healthy and faulty signal which can be verified after comparison to detect the fault. III. C ONCLUSIONS In this paper a useful as well as informative survey has been carried out on GIS failures causes, condition monitoring techniques, sensors for detection and data analytics to analyse the sensor acquired data. Several phenomena related to Electrical, Mechanical, Thermal, Chemical as well as Optical failure of GIS has also been discussed. Online Onsite CM is considered as the most viable combination for GIS. Details of different sensors supporting this CM combination have been covered in the discussion. Moreover a review; based on development of modern signal analysis techniques; which are helping in CM of different kinds GIS failure either in the process of early fault detection, classification or feature extraction; has also been performed. R EFERENCES [1] M. Al-Suhaily, S. Meijer, J. J. Smit, P. Sibbald, J. Kanters, and T. Tso, “Criticality assessment of GIS components,” in 2010 International Conference on High Voltage Engineering and Application, Institute of Electrical and Electronics Engineers (IEEE), oct 2010.
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