IET Generation, Transmission & Distribution Research Article
Artificial intelligence-based short-circuit fault identifier for MT-HVDC systems
ISSN 1751-8687 Received on 20th September 2017 Revised 14th January 2018 Accepted on 30th January 2018 E-First on 13th April 2018 doi: 10.1049/iet-gtd.2017.1345 www.ietdl.org
Ahmed Hossam-Eldin1, Ahmed Lotfy2, Mohammed Elgamal3, Mohammed Ebeed4 1Electrical
Engineering Department, Alexandria University, Alexandria, Egypt and Control Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt 3Operation Services Department, Arab Petroleum Pipelines Co. SUMED, Alexandria, Egypt 4Electromechanical Maintenance Department, Alexandria Port Authority, Alexandria, Egypt E-mail:
[email protected] 2Electrical
Abstract: The most convenient solution to link faraway significant renewable energy sources (RESs) is the voltage-source converter multi-terminal high-voltage DC systems (MT-HVDC). However, to maintain system stability and continuity of supply, a rigid and fast fault locating technique is required. This study proposes a novel inherent travelling waves based short-circuit DC fault identifier, which accurately identifies both of the fault location and faulty pole in multiple numbers of cables in MT-HVDC system using a single current sensor. Both of a discrete wavelet examiner and a fuzzy-neural pattern recogniser precisely spot the faulty line and fault location based on the mutual effects of short-circuit initiated travelling waves between lines belonging to the same loop. A software toolbox is structured to illustrate the adequacy of the proposed artificial intelligence technique. This method is valuable to MT-HVDC administration centres, particularly those concerned with long-distance RES.
1 Introduction Power system planners use renewable energy source (RES) as a strategic solution to cover the lack of electrical energy sources. To meet electric power requirements, a large number of long-distance RES ought to be built [1]. The voltage-source converter (VSC) has been used as the convenient solution to link these offshore RES [2– 4]. MT-high-voltage DC (HVDC) is eligible to connect multiple numbers of VSC together using their DC side as presented in Fig. 1 [5, 6]. However, the main weakness of the VSC-multi-terminal (MT)-HVDC systems is the vulnerability toward DC short-circuit faults, which affect the whole system's stability [7, 8]. Consequently, appropriate operation of MT-HVDC systems relies on its ability to ride-through such worst-case situation. A technique that utilises the difference between pre- and post-fault measurements to identify faulty lines in MT-HVDC systems was reported [9]. The fault current is suppressed through disconnecting the AC side of VSCs, and further isolating the fault by fast DC switches. This method degrades the reliability of MT-HVDC, as the entire system is disconnected whenever a single line encounters a fault. A more effective fault detection method was proposed based on analysing the measured voltage data at each line using a continuous wavelet transform (WT) algorithm [10]. However, this
method is only valid for the less possible to occur positive to negative faults. DC circuit breakers topologies applications in MTHVDC for clearing the faults in addition to the network recovery strategies after clearing the faults using AC circuit breakers were newly proposed. However, they adopted traditional fault identification methods such as pre-fault and after fault measurements and measuring the voltage drop across a series inductance [11–16]. The present authors proposed a travelling wave-based technique applicable for any fault type, for discovering the fault location and type in MT-HVDC systems [17]. Nevertheless, the mutual effects of travelling waves between lines that belong to the same loop were not studied, which affected the precision of fault location. In this paper, to overcome drawbacks of the mentioned techniques, a new artificial intelligence (AI) MT-HVDC precise fault identifier is proposed to accurately spot faulty lines and define both of the fault type and the precise fault location through recognising the impact of travelling waves. The mutual effects of the fault current travelling waves, which travel from the faulted line to the neighbouring lines, are used to identify the faulted pole and the location of the fault using the minimum number of current sensors, one sensor for each loop. Measured current data is analysed via a discrete WT (DWT), a fuzzy classifier further categorises the wavelet data into pattern classes, and finally trained neural networks (NNs) recognise the patterns of the impact of the travelling waves on the wavelet data. Applications of DWT and travelling wave techniques are discussed in Sections 2 and 3 and the detailed proposed approach and its simulation are presented in Sections 4 and 5. Results and discussion are included in Section 6.
2 Wavelet transform Wavelet examination is a robust signal processing technique, able to discover rapid changes in signals. WT produces multiple resolutions approximation and detail coefficients, supported in time and frequency framework. Accordingly, it is suitable to analyse periodical, noisy, sporadic, and transient signals such as travelling waves [18–21]. The discrete mother wavelet is devised as [22, 23] Fig. 1 MT DC system [9] IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
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Am + 1, n =
1 ∑ ck Am, 2n + k 2 k
(2)
dm + 1, n =
1 ∑ bk Am, 2n + k 2 k
(3)
bk = −1 kcN − 1 − k
(4)
The re-modelled signal approximation (X) at scale M is formulated as xM t = AM , nϕM , n t
(5)
The rebuilt signal detail (D) at scale index ‘m’ is presented as Dm t =
2M − m − 1
∑
n=0
dm, nψ m, n t
(6)
where Ck is the scaling coefficient, N is the number of scaling coefficients, bk is reconfigured coefficient, and M is the maximum number of levelling.
3 Travelling waves Incident duration time of travelling waves is few milliseconds. A rapid fluctuation in voltage at any location in the line produces electromagnetic waves that spread in adverse directions to the terminal substations. The precision of a travelling wave-based fault locator counts on the appropriate estimation of wave velocity through the line. Wave diffusion speed counts on line specifications, influence of weather, and the route of wave diffusion. Accordingly, the speed of travelling waves produced by earth faults is less than that produced by other faults [29, 30]. Fault locators based on travelling waves were categorised to five types: ‘A’, ‘B’, ‘C’, ‘D’, and ‘E’ based on the measuring methods [31, 32]. The travelling wave fault locators have been exclusive to AC transmission lines. The method embraced in this paper is a mutation of the E-type fault locator, which is the most suitable as it is installed on one end of the transmission line. The Etype is similar to the impulse reflectometry method that is used for underground cables. It measures the waves produced by opening and reclosing the AC circuit breaker. According to the difficulties of extinguishing the DC faults, the waves produced by fault projection are used as a substitution of injecting travelling waves. Consequently, the fault position is formulated as [Df ]E =
(7)
[Df]E are fault positions produced by fault locator ‘E’, ‘t1’ is the time of the first wave, ‘t2’ is the time of the second wave, and ‘v’ is the diffusion speed.
Fig. 2 Fault characteristics (a) Fault current characteristics, (b) Voltage of the capacitor characteristics, (c) Travelling waves effect on faulted line's wavelet, (d) Travelling waves effect on neighbour lines’ wavelet
ψ m, n t =
t2 − t1 v 2
1
2m
ψ
t − n2m 2m
(1)
Both of ‘m’ and ‘n’ are integers that control the dilation and translation of the mother wavelet, respectively, which are also termed ‘decomposition level’ and ‘sampling number’ respectively. The mother wavelet is selected to widely resemble the signal under examination. Accordingly, Daubechies mother wavelet is adopted is this paper. The detail coefficient (d) and approximation coefficient (A) of DWT are formulated as [24–28]
IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
4 Detailed proposed approach To recognise the faulty line and the fault location in MT-HVDC using one sensor for several lines, the current measurements of positive and negative sensors are examined using DWT to study the impact of the travelling waves resulting from fault occurrence. The ‘db4’ is used as mother wavelet to decompose for two levels. Under typical operating circumstances, the value of wavelet detail D2 remains steady nearly zero. When short-circuit fault occurs, the amplitude of wavelet detail is rapidly increased at the instant of fault projection as a result of the rapid changes in the current and capacitor voltages as presented in Fig. 2. Second, all the wavelet data is then passed to a fuzzy algorithm that classifies the data collection in different classes according to fault pattern similarity. Finally, classes of the fault patterns are used to train NNs to recognise the fault patterns and consequently identify the faulty line in addition to the faulty pole. 2437
Fig. 3 Three-terminal MT-HVDC
4.1 Fuzzy C-mean classification In fields that deal with diagnoses, it is required to find the structures or the patterns in the data obtained from observation. By finding the pattern, the data are easily classified according to similar patterns, attributes, or features. This process is known as classification or clustering. Accordingly, all wavelet data have been passed to the fuzzy C-mean algorithm, which classifies the collected data sets into classes according to fault patterns’ similarity. The faults with similar patterns belong to the same class. To describe the C-mean method adopted to determine the fuzzy S-partition matrix U, an objective function Jh is defined as [33, 34] y
s
∑∑
Jh U, v =
k=1i=1 h
∑
dik =
j=1
μik
h′
dik
2
(8)
verifying. Thus, the classes produced by the fuzzy C-mean have been used to train NNs to recognise the fault patterns in MTHVDC. Multi-layer feed-forward NNs have been selected in this paper to recognise the fault projection and the faulty line. Furthermore, single-layer feedback networks have been used to assure the faulty line. Backpropagation algorithm is adopted to train the NNs. The backpropagation algorithm is divided into two processes: forward and backward processes; in the former, the output of the NN, as well as the outputs of each neuron, is obtained. In the backward process, the weights are updated according to its share in the error backpropagation. The appropriate neuron activation function for pattern recognition is a sigmoid function, which is formulated as [37, 38] oij + 1 =
xki − vi j
2
(9)
1 n i i 1 + e− ∑k = 1 ωk j ok − θ
The error of the NN is formulated as
where ‘y’ is the number of data sets, ‘s’ is the number of classes, μik is the membership of the kth data point in the ith class, ‘h’ is the number of coordinates required to describe the data sample location in the space. The cluster coordinates for each class are formulated as [35, 36]
eij = oactual − ooutput
∑k = 1 μikh′ xki
(10)
y ∑k = 1 μikh′
The fuzzy clustering can be formulated as an optimisation problem as follows: Jh∗ U ∗, v∗ = min J U, v
(11)
As with many optimisation processes, the solution is not guaranteed to be the global optimum. Consequently, iteration optimisation algorithm, which is eligible to obtain the best solution available within a pre-specified level of accuracy, is adopted in this paper. The iterative optimisation algorithm is summarised as: Fix ‘s’ (2 ≤ s ≤ n) and select a value for the parameter m′. Initialise the partition matrix U 0 . Each step in this algorithm will be labelled r, where r = 0, 1, 2, … ii. Calculate the c centre {vir} for each step. iii. Update the partition matrix for the rth step, U r, as follows:
δij − 1 = oij − 1 1 − oij − 1
=
c
∑
dikr
r j = 1 d jk
2/(m′ − 1)
−1
(12)
iv. If U r + 1 − U r ≤ εL, stop; otherwise, set r = r + 1 and return to step 2. 4.2 Neural network Artificial NNs mimic the human neurons concept to accomplish the machine learning and patterns recognition using weighted connections and nodes activation functions. The learning process of the artificial NN is divided into two stages: training and 2438
n
∑ ωki j
δij
−1
k=1
i = 2, 3, …
(15)
The weight correction is formulated as ωijk− 1 p + 1 = ωijk− 1 p + βΔωijk− 1 p − 1 + Δωijk− 1 p
(16)
Δωijk− 1 p = αδkioijk− 1
(17)
where p is the iteration number, β is the momentum constant 0 ≤ β ≤ 1 , and α is the training rate. Moreover, the overall error is calculated as
i.
μikr+ 1
(14)
where ‘Oactual’ and ‘Ooutput’ are the required and network outputs, respectively. The error gradient for the neurons is formulated as [39, 40]
y
vi j =
(13)
E=
n
M
∑ ∑ ej
p=1 j=1
2
p
(18)
To speed up the training process, variable training rate, α, has been embraced. The training rate is updated according to the following rules: • If E p < E p − 1 , then α = 1.05α • If E p > E p − 1 , then α = 0.6α
5 Simulation To test the protection methodology, a three-terminal VSC-based MT-HVDC is adopted as shown in Fig. 3. The used current sensor is placed at station ‘1’. The parameters of the transmission lines are illustrated in Table 1. The model of VSC proposed in [41–50] has been adopted, vector control, and traditional proportional–integral controller. To obtain the training table of the NN, the system has been simulated IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
Table 1 Transmission lines’ parameters Parameter Line 1 Line 2
Line 3
resistance, km inductance, km
0.01273
0.01273
0.01273
1.9337 × 10−3
1.9337 × 10−3
1.9337 × 10−3
capacitance, km
12.74 × 10−9
12.74 × 10−9
400
300
12.74 × 10−9 300
length, km
Fig. 5 Neural network training programme Fig. 4 Fuzzy clustering programme
for regular and irregular circumstances for 300 kV, 600 A network. The stored data of simulation are then analysed using M-files programmes to produce the patterns of faults. The fuzzy clustering and NN training programmes are summarised as presented in Fig. 4 and Fig. 5.
6 Results and discussion The mutual effect of travelling waves between multiple lines that belong to the same loop has been studied. First, The D2, level 2 detail wavelet coefficient, of the measured current data has been obtained under regular and faulty circumstances in variant locations in the three lines. According to the sampling frequency, 50 kHz, the frequency interval of level 2 detail coefficient is from 12.5 to 6.25 kHz. The simulation results illustrate that D2 remains stable roughly zero under regular circumstances as presented in Fig. 6a. When a fault occurs, the impact of travelling waves causes the magnitude of the D2 to rise rapidly. It has been noted from a simulation that the D2 value is significantly high, from 60 to 100 as presented in Fig. 6b (case of the fault in line 1). The value of D2 is between 0.5 and 1.2 for a fault in line 2 and between 0.9 and 3 when the fault in line 3 as presented in Figs. 7a, b and 8a, b. Consequently, D2 data is passed to the C-mean fuzzy classifier, which classifies the influence of the travelling waves into eight patterns, with fuzzy membership function values between 0 and 1 for each element. These eight patterns contain four patterns for line 1 and four patterns for both lines 2 and 3 together. According to the fuzzy classifier data, line 1 has three normal circumstances patterns and one pattern for faulty circumstances. In addition, each of lines IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
2 and 3 has a pattern for normal circumstances and another for faulty circumstances. Three NNs have been designed to recognise the faults projection patterns created by the fuzzy classifier. The first network tackles fault projection patterns in line 1 and the second is dedicated to fault projection patterns in both of lines 2 and 3. The third network decides which line is faulty based on the output of the first two networks. The fuzzy classifier output data have been divided into three sets: training, validation, and testing. The first network containing five hidden layers, each with 35 neurons, was trained after 16,000 epochs with training rate between 0.012 and 0.001 as presented in Figs. 9 and 10a. The NN, containing two hidden layers, each with 75 neurons, learned the patterns in 25,000 epochs as presented in Fig. 10b. Both first and second NNs have been trained based on the outputs of the fuzzy clustering. The proposed algorithm is able to discover the fault location with negligible error as presented in Tables 2 and 3. The proposed algorithm is summarised as presented in Fig. 11a. Moreover, resistance has no influence on the proposed approach accuracy as presented in Fig. 11b. A comparison between the algorithm proposed in [17] and the algorithm in the present paper was performed. The AI algorithm proposed in [17], the fuzzy logic voter, has been developed to allow such comparison as shown in Table 4. Although both algorithms yielded the same fault location results as shown in Table 5. The present algorithm was found to be faster than the developed version of [17]. The time required to identify a fault occurrence was found to be only 0.38 ms instead of 0.6 ms. The in attributed to the fast action of the fuzzy-neural pattern recogniser used in the present algorithm. 2439
Fig. 6 Line 1 D2 (a) Normal circumstances, (b) Under positive to ground fault
Fig. 7 D2 of line 3 from current sensor of line 1 (a) Fault at 50 km, (b) Fault at 100 km
Fig. 8 D2 of line 3 from current sensor of line 1 (a) Fault at 150 km, (b) Fault at 200 km
7 Conclusion To accomplish adequate protection for MT-HVDC systems, conventional techniques are not applicable. Instead of injecting travelling waves, those produced by fault occurrence are used. 2440
Wavelet examination was applied to decompose both positive and negative currents. The details were further investigated to discover the faulty line using a fuzzy classifier to discover the fault patterns. A group of three NNs was designed based on the data created by the fuzzy classifier. Consequently, both of the faulty line and the IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
Fig. 9 First NN's training rate
Fig. 10 Second NN's error epoch relationship (a) First NN, (b) Second NN
Table 2 Fault location in line 1 using current sensor installed in line 1 Actual fault location, km Detected fault location, km 50 75 100 150 200 250 300
49.999 74.999 99.999 150 200 250 300
Table 3 Fault location in line 3 using current sensor installed in line 1 Actual fault location, km Detected fault location, km 50 100 150 200
type of fault were precisely identified. The proposed method is capable to determine accurately the fault location with an insignificant error. Reduction of error as the fault distance increases is due to that the travelling wave gets more steady (away from transients) and thus can precisely be identified by the fuzzyneural pattern recogniser. Accordingly, identification is more IET Gener. Transm. Distrib., 2018, Vol. 12 Iss. 10, pp. 2436-2443 © The Institution of Engineering and Technology 2018
49.999 99.999 150 200.21
Error 0.002% 0.0013% 0.001% 0 0 0 0
Error 0.002% 0.001% 0 0
accurate rather than having a short distance fault that will provide a nascent travelling wave full of spikes and transients.
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Fig. 11 Summarised algorithm (a) Summary protection algorithm, (b) Fault's resistance and accuracy
Table 4 Voter fuzzy set Normal D2 interval
[0:0.5]
Line 2 faulted
Line 3 faulted
Line 1 may be faulted
Line 1 faulted
[0.3:1.2]
[0.9:3]
[3:40]
[30:90]
Table 5 Comparison between calculated fault locations in kilometres Actual fault location By developed algorithm of [17] 50 75 100 150 200 250 300
49.999 74.999 99.999 150 200 250 300
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