Fault Location Estimation in HVDC transmission line

Fault Location Estimation in HVDC transmission line using ANN Jenifer Mariam Johnson1 and Anamika Yadav1 1

Department of Electrical Engineering, National Institute of Technology, Raipur, 492010, C.G., India [email protected], [email protected]

Abstract. This paper presents a simple, yet accurate method for fault location in HVDC transmission lines using Artificial Neural Networks. The ±500𝐾𝑉 HVDC system has been modelled using PSCAD/EMTDC and further analysis has been done using MATLAB. Single-end AC RMS voltage and DC voltage and current have been used to identify the location of the fault. This method is relatively simple because the standard deviation of the fault data alone gives acceptably accurate results while using ANN. Keywords: ANN, fault location, HVDC transmission line, PSCAD/EMTDC.

1 Introduction High Voltage DC transmission systems are being extensively used now owing to the ever increasing need for electric power and hence their bulk transmission over often very long distances [1]. Such transmissions may even involve transfer of power between asynchronous systems. The HVDC technology comes as an economic solution, in comparison to HVAC, to both these issues for transmissions beyond a certain break-even distance [2-4]. Faults in the transmission line are frequent and at the same time need to be cleared as soon as possible to ensure the continuity of power transfer and to avoid economic losses. Since HVDC technology is preferably used for transmission line lengths above the break-even distance, which is as high as 800km, the accurate and fast location of faults, once they have occurred, is very important for the reliability and efficiency of the system. The present-day primary protection schemes for HVDC transmission lines employ travelling wave based methods, whereas the back-up protection is based on dc minimum voltage and dc line differential protection methods. These schemes, however, are not sufficiently accurate or sensitive and hence are less reliable. The present scenario hence demands better line protection techniques that would ensure better reliability that is a key issue due to the extensive usage of the HVDC technology these days. The travelling wave algorithms estimate the fault location based on the time taken by the fault generated traveling wave to propagate along the transmission line [5-8]. In methods requiring two-terminal data, the global positioning system (GPS) is

usually employed to keep the measurements synchronized. The traveling wave theory follows that the transients that are generated as a result of faults or switching procedures are composed of traveling waves that continue to bounce back and forth between the fault point and the terminals until a post-fault steady state is reached. The signals intended to be used need to be synchronized before the traveling wave algorithm is applied. Once the signals have been synchronized, the traveling wave algorithm is used to estimate the travel time of forward and backward fault transients along the concerned transmission line between the fault point and the location of the relay. The fault location is then estimated using these travel times. Although these methods have fast response and high accuracy, the accuracy is affected by the accurate detection of the surge arrival time. However, in methods requiring only single terminal data, there is no need of GPS and these methods are hence more economical [9-12]. But they require the detection of secondary reflection wave as well. A single ended fault detection and location method for HVDC transmission systems using DWT was first proposed in [9]. A two-ended traveling wave-based fault location method for an HVDC transmission system was put forth and implemented in [10]. A two-ended fault location method for overhead HVDC transmission line using steady-state voltages and currents was proposed in [12]. In this paper, a relatively simple fault location method has been proposed that uses the AC RMS voltage and DC current and voltage at a single end of the HVDC transmission line. A ±500𝑘𝑉 HVDC system was modelled using PSCAD/EMTDC and a single line to ground fault was simulated on the positive line at different locations from the rectifier end to the inverter end and the data was collected and recorded. This data was analyzed in MATLAB and it was found out that the rectifier end AC RMS voltage, DC current and DC voltage of the faulted line alone were sufficient to determine the location at which the fault occurred. The standard deviations of these measurements for each location were then used to train the Artificial Neural Network. The accuracy of this technique was then verified by testing the network with data obtained with different fault locations than those given for training. The accuracy obtained was ±2 km (0.21%).

2 HVDC Transmission A 936km long ±500kV transmission system has been modelled in PSCAD/EMTDC for further analysis. The CIGRE Benchmark HVDC model which is monopolar was modified to make it bipolar as in Fig.1. The tower structure of the ±500 kV HVDC transmission line is also depicted in Fig.2, which has been adopted from [13].

Smoothing reactor

Rectifier side

Smoothing reactor

Inverter side

936 km

AC System (rectifier side)

AC System (inverter side)

936 km

Smoo thing reactor

Smoothing reactor

Fig.1. Bipolar HVDC system model

Fig.2. Tower structure for ±500 𝑘𝑉 HVDC lines

2.1 DC Transmission line fault The common types of faults that are likely to occur in HVDC transmission lines are the single line to ground and line to line fault. Moreover, the single line to ground fault can occur either on the positive or on the negative polarity transmission line. For our analysis, a single line to ground fault has been simulated on the positive polarity transmission line, assuming the remaining two fault characteristics to be of similar nature. A permanent fault is simulated on the HVDC transmission like at every kilometer from 1km to 935km. A half of this data is utilized for training the neural network and the remaining to test the same network.

3 Artificial Neural Network An ANN is an artificial (man-made) system motivated by research into the human brain. It mimics the operation of the brain in performing a particular task [14]. The brain has many features that are desirable in Artificial Intelligence systems:  it is robust and fault tolerant; nerve cells in the brain die every day without affecting its performance significantly  it can deal with information that is fuzzy, probabilistic, noisy or inconsistent  it is highly parallel  it is small, compact and dissipates very little power The building block of any ANN is a very simple model of the fundamental cell of the brain -the neuron. Neurons are connected to form networks with different structures to perform certain functions. The manner in which the neurons of a neural network are structured depends on the learning abilities and functions that we want to achieve.

Fig.3. Neural network architecture used

The data extracted from the PSCAD/EMTDC model of the HVDC transmission system is used as the input and target data for training the neural network. In particular, the rectifier end AC RMS voltage and the DC voltage and current values, also on the rectifier end, on the faulted line are used here. The standard deviation of these fault data over a sufficient pre-fault, during-fault and post-fault duration are fed as the input data to the neural network in our method. The corresponding distances act as the target for the network. The neural network is trained using the LevenbergMarquardt backpropagation algorithm. The main goal of designing the neural network is to accurately estimate the fault location along the transmission line under consideration. For this, the network is initially trained with a set of training data set consisting of extracted samples of prefault and post-fault AC RMS voltage, DC voltage and current measurements taken at the rectifier end of the concerned transmission line from different fault case simulation, along with the corresponding targets, being the targeted fault location. The neural network architecture used in this paper consists of two layers (one hidden and one output) of 20 and 1 neuron respectively, as shown in Fig. 3. The activation functions used for the layers are tangent sigmoid and linear function respectively. The

three inputs given to the network are the AC RMS voltage, DC voltage and current at the rectifier end. The output is the fault location corresponding to the input data given. Once trained, the network is now ready to predict the fault location for any data including those for which it has not been trained with.

3 Results The fault data that had earlier not been used for training the neural network is used to test the same network to determine how well the neural network used can identify the fault location. The test data consist of total 468 fault cases simulated at every 2km from 2-934km length. It is found out that the network that predicts the fault location with an accuracy of ±2 km (0.21%), which is quite acceptable with respect to the length of the transmission line under consideration. Table 1 illustrates few test results along with the error statistics of proposed fault location algorithm implemented using ANN. The error plot has been depicted in Fig.4 which shows the different between the estimated and target fault location for 468 test fault cases. It can be observed that the error is more at the sending end from where voltage and current measurements have been made. The minimum error obtained is 4.633e-5(4.95e-6%) for fault at 164 km whereas the maximum error obtained is 1.9908(0.213%) for fault at 54 km. 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑒𝑟𝑟𝑜𝑟 =

𝐴𝑐𝑡𝑢𝑎𝑙 𝑓𝑎𝑢𝑙𝑡 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛−𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝐿𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑙𝑖𝑛𝑒

𝑥100

1.5

1

0.5

0

-0.5

-1

-1.5

-2 0

50

100

150

200

250

300

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450

Fig.4. Error plot obtained using ANN

500

(1)

Table 1. Error statistics obtained using ANN

Actual Fault Location (km)

Fault location estimated with ANN (km)

2 8 10 20 42 50 54 62 100 150 200 300 400 500 600 700 800 900 910 920 930 934

1.883 8.4543 9.6167 19.8986 43.1579 50.2751 55.9907 61.1674 100.0509 149.9481 200.0856 299.927 400.0856 500.0427 599.927 699.8462 800.0639 900.0382 909.8746 920.1917 929.8417 933.9065

Error (km) 0.1170 -0.4543 0.3833 0.1014 -1.1579 -0.2751 -1.9907 0.8326 -0.0509 0.0519 -0.0856 0.0730 -0.0856 -0.0427 0.0730 0.1538 -0.0639 -0.0382 0.1254 -0.1917 0.1583 0.0935

Percentage Error (%) 0.0125 -0.0485 0.0409 0.0108 0.1237 -0.2939 0.2126 0.0889 -0.0054 0.0055 -0.0091 0.0078 -0.0091 -0.0045 0.0077 0.0164 -0.0068 -0.0041 0.0134 -0.0204 0.0169 0.0099

4 Conclusion The proposed method is thus a relatively simple method for the prediction of fault location in a bipolar HVDC transmission system. The simplicity lies in the fact that the voltages and current data obtained can be used in the neural network by just taking the standard deviation of the fault data for fault at various locations. The accuracy is ±1 km which can be further improved if we take the inverter end data along with the rectifier end data. Although this option provides better accuracy, it has been avoided owing to the additional communication channel requirement that would be involved if the receiving end and sending end data were both required for the fault location algorithm.

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