Faults Recognition, Identification and Localization in Smart Distribution Networks – A Review A.K. Onaolapo Dept. of Electrical Power Engineering, Durban University of Technology, P O Box 1334, Durban, 4000, South Africa. K.T. Akindeji Dept. of Electrical Power Engineering, Durban University of Technology, P O Box 1334, Durban, 4000, South Africa. E. Adetiba Department of Electrical & Information Engineering, College of Engineering, Covenant University, Canaanland, Ota, Nigeria.
[email protected] South Africa
Abstract Occurrence of faults in Distribution Networks (DN) affects the reliability and security of supply when not managed timeously. Faults location in the traditional DN takes longer than necessary leading to economic losses to both power utility and consumers. The advent of Information and Communication Technology (ICT) and Intelligent Electronic Devices (IEDs) into the DN has made it smarter, therefore, better and safer. This paper is a review of several existing methods proposed in the literature for recognizing the existence of faults in a network, identifying the types of faults and locating its area of occurrence in order to make it more effective and efficient. The paper also gives a summary of an ongoing research on the application of Machine Intelligence Techniques (MIT) in Smart Distribution Networks (SDN) to recognize and locate faults. Key words: Smart Distribution Grid, Fault, SCADA, Advanced Metering Infrastructure, Fault Diagnosis Scheme, Fault Management Activities.
1. INTRODUCTION Smart Grid (SG) is a power system which is more dependable, cost-effective, eco-friendly, with better safety and security features. It is an innovative form of power grid, for its upgraded energy efficacy, controlled emission, optimized utility, outlined demand and decreased cost [1]. The European Regulators Group for Electricity and Gas defined the smart grid as an electricity network that can efficiently integrate the behavior and actions of all users connected to it –generators, consumers and those that do both – in order to ensure economically efficient, sustainable power system with low losses and high levels of quality and security of supply and safety [2]. Smart grid is likewise famous for its self-healing abilities. Grid self-healing embraces the skills of self-detection, self-identification, self-decision and self-restoration, and makes the operations of distribution systems in diverse conditions, safe and reliable. Under extreme situations, separation from the main grid is achievable via self-healing. In this situation, continuous operation with a distributed power and power storage scheme is realized [3]. Faults in DN cause outages and bring about reliability and Power Quality (PQ) issues such as voltage sags, momentary and sustained interruptions, and increased operational costs [4]. The management and detection of outage has been an established problem in power distribution systems. The societal and economic costs owing to loss of loads from distribution outages have been progressively severe. Nevertheless, a number of methods have been proposed for continuous monitoring and fault detection, This work was sponsored by Durban University of Technology (DUT) bursary scheme (RFAEnergy) 2016 and National Research Fund (NRF), South Africa, under DST-NRF research grants UID-105584:2016 & UID-109775:2017
identification and location. Fault detection, identification, and location typically needs be solved as an integrated problem in power systems. It can be predicted that a multifunctional method, which can do fault detection, identification and location, will have an extensive array of applications in SG systems [5]. The advent of Information and Communication Technology (ICT) and Intelligent Electronic Devices (IEDs) into the DN has made it smarter, therefore, better and safer. ICT defines the essence of unified communications [6] and the incorporation of telecommunications (telephone lines and wireless signals), computers, innovative software, middleware, storage, and audio-visual systems, which allow users to access, transmit, manipulate and store information [7]. The broadness of ICT covers any product that will retrieve, transmit, manipulate, or store information electronically in a digital form, e.g. email, computers, digital television, robots. An IED is a microprocessor-based controller of power system equipment, such as circuit breaker, transformer and capacitor bank. IEDs receive data from sensors and power equipment, and can issue control commands, such as tripping circuit breakers if they sense voltage, current, or frequency abnormalities, or raise/lower voltage levels in order to achieve the preferred level [8]. Examples of IEDs are protective relaying devices, circuit breaker controllers, On Load Tap Changer controllers, recloser controllers, capacitor bank switches, voltage regulators etc. A typical IED contains up to 5-12 protection functions, an auto-reclose function, 5-8 control functions, communication functions, self-monitoring function, etc. Hence, the name - Intelligent Electronic Devices. Recently, much research works have been devoted to fault diagnosis, identification and location, and a range of techniques, algorithms, and models have been proposed [9]. The focus in this paper is upon a review of Fault Detection, Isolation, Location and Recovery (FDIR), regularly considered “self-healing” ability [10, 11]. 2. OUTAGE DETECTIONS AND FAULT DIAGNOSIS SYSTEMS Various efforts have been exercised by researchers in finding answers to the questions of outage and fault detection and identification. For outage detection and fault diagnosis, fuzzy set methods, neural networks and real-time measurement using a single sensor at the substation have been recommended. The outcome of using the hybrid fuzzy–genetic method is the production of high-quality fault detection in a reasonable time for a large-scale system [12-14]. Knowledge based methods that pool diverse kinds of information Supervisory Control and Data Acquisition (SCADA), customer calls, Advanced Metering Infrastructure (AMI) polling are also available. As a result, an outage locating and confirmation knowledge-based system was designed, developed and verified [15-17]. Formerly, methods for trend extraction and similarity estimation were proposed. In [18], a distance metric dependent similarity measure for fault classification, was debated and also transient voltage stability and voltage sag [5, 19] for fault detection was proposed. Using decision trees [20] and syntactic pattern recognition [21] for trends classification has been discussed. Dynamic Time Warping (DTW) [22, 23], was used on the array of events for similarity estimation. Dynamic trend analysis depends on the fact that process signals can be denoted at diverse levels of detail [24, 25] and that related events result in qualitatively related trends. The simulated results undoubtedly show that the proposed methods can accurately differentiate between transient voltage stability and voltage sag in power system protection. [26, 27] A line and double line outage detection methods were designed in [28] and [29] respectively. The results show that information of topology changes outside of the local control area could be acquired by using data which is presently available. Also, the presence of measurement noise and oscillatory behavior has no catastrophic effect on the algorithm’s performance. A power network adaption of the worst configuration heuristics combined with linear programming algorithm is developed in [30] to forecast power grid weak points. Four feature selection methods, hypothesis test, stepwise regression, stepwise selection by Akaike’s information criterion, and Least Absolute Shrinkage and Selection Operator/Adaptive Least Absolute Shrinkage and Selection Operator (LASSO/ALASSO) are compared in [31] in terms of their model requirements, data assumptions, and computational cost for fault identification. Fault diagnosis performance by accuracy, probability of detection and false alarm ratio were compared using real-world
datasets from Progress Energy Carolinas. A fault detection and identification method is proposed based on Petri net which is designed to capture the simulation parameters of the protection system of the distribution network. One finding is that, if the normal operational mode of the grid is suitably healthy, the instantons (or failure modes), are suitably sparse [9]. For fault diagnosis and detection, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are considered in [32, 33], the experimental outcomes indicate high accuracy when the ANN and SVM classifier were trained on data from a real power network and test data originated from simulated data. A lower accuracy resulted when the ANN and SVM classifier were trained on simulated data and test data originated from the power network. Expert system techniques are studied in [34-36] the results validate that Bayesian network has a favorable potential for application along with the voltage/current location approach to feeder fault location. Diverse techniques for more broad systems have been pointed out. A smart supervisory coordinator in dynamic physical systems; a mode identification technique for hybrid system and a scheme of applying wireless sensor networks for energy monitoring and fault diagnostics for industrial motor systems were proposed. Experimental results proves that these methods can function in the real-time industrial environments. [37-39]. A microprocessor data acquisition system showing that constant impedance loads deliver a sufficient load model for High Impedance Fault (HIF) calculations is developed and installed. Additionally, the employment of discrete wavelet transform (DWT), Bayesian selectivity, artificial intelligence and harmonics for HIF detection have been proposed. The techniques behavior have been experimented over a wide area of the simulated network utilizing the concept of allocated wireless sensors. Sensitive and secure detection of the faults due to leaning trees has been attained [40-42] 3. SMART DISTRIBUTION GRID FAULT LOCATION TECHNIQUES After discovering and identifying a fault in SG, then comes the tasks of locating its geographic position and estimating its area of impact [5]. Using fault distance computation that employs impedance information in [43] a fault location technique was proposed. Intelligent methods were utilized in locating the fundamental faults by determining the status of the protective relays. The field test results was encouraging and validate the promising potential of the employed algorithms for practical use [16, 44]. Jiang et al [5] employed computational methods that are data-based by utilizing a Frequency Disturbance Recorder (FDR) for fault location. The resultant multimodal information is recorded by distributed FDRs as soon as a fault takes place. This in turn makes it easy to diagnose the fault type and locate the geographic area, thereby improving the response time, preventing cascading outages and enhancing the reliability of the SG system [45]. Gaussian Markov Random Field (GMRF) fault location technique was applied in [46] and [47] based on phasor angle measurements across the buses in SG. Employing the maximum Wavelet Coefficients (WCs) of the frequency and voltage signals under fault disturbance, a wavelet-based method is designed. The analysis for the results shows the effectiveness of (wavelet transform) WT-based MRA (multiresolution analysis) on data compression and denoising for disturbance signals. [48] and [49], the accuracy of load and fault location estimation was shown to improve by the relationship between the WCs and power variation. Fault location proposed in [5] is by voltage signal feature extraction using the Matching Pursuit Decomposition (MPD) with Gaussian atom dictionary. First comes the MPD feature extraction, then a hybrid clustering algorithm; used to cluster the feature vectors into several subsets. After which the outcomes of the hybrid clustering are pooled with the SG topology to produce the fault location and contour map. 4. SYSTEM RELIABILITY WITH SMART GRID TECHNOLOGY Large-scale fault events and their resultant effects on the overall stability of the power grid is a fundamental challenge on the reliability of power systems. Unfortunately, in today’s power systems, there are no sufficient fault diagnosis mechanisms against countless malicious attacks and likely physical events [47, 50]. Hence, an urgent need for quick assessment and correction of faults against cascading events is required.
In [51], the effects of Fault Diagnosis Scheme (FDS) types – the Reactive Fault Diagnosis Scheme (RFDS) and the Proactive Fault Diagnosis Scheme (PFDS) were compared for the reliability performance of the electric power distribution systems. A quantitative reliability assessment studies was conducted on a typical Finnish urban distribution network employing FDS using Smart Grid Simulator (SGS). The comparative case studies results showed that the reliability performance of the electric power distribution systems is improved by employing either the RFDS or the PFDS. However, when employing the PFDS, the improvements are better pronounced. This is because the PFDS can diagnose the components while in the process of failing (i.e prior to breakdown) and therefore helps in reducing the frequency and duration of outage experienced by customer while the RFDS can detect and locate the components after the failing (i.e. already deteriorated); and therefore impacts only on the duration of outage experienced by customer. The authors employed case studies 1 - 3 for a typical Finnish urban distribution network so as to evaluate the reliability performance of the test system. Their resultant indices are expressed in Table 1 below. Cases 1, 2 and 3 show the reliability performance of the test system when there’s no automation scheme for health monitoring; when a typical Reactive Fault Diagnosis Scheme (RFDS) is implemented in the system and when a typical Proactive Fault Diagnosis Scheme (PFDS) is implemented in the system respectively. The result of the impacts of the RFDS and the PFDS on reliability performance of the test system is shown in Table 1. The five reliability indices utilized as shown in the Table are the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), Average System Unavailability Index (ASUI), Expected Energy Not Supplied (EENS), and Expected Cost of the power interruptions imposed on the customers (ECOST). For Case 2, other reliability indices improved except SAIFI which remains unchanged. But, for Case 3, all the reliability indices improved. This table shows that the PFDS has much better reliability performance in the test system than the RFDS. Table 1: System oriented reliability indices of the test system [51] Reliability Index SAIFI (intr/sub-yr) SAIDI (hrs/sub-yr) ASUI (%) EENS (kWhr/yr) ECOST (EUR/yr)
Case 1 0.166 0.735 0.00839 3741.73 94198
Case 2 0.166 0.161 0.00184 765.81 20595
Case 3 0.070 0.116 0.00132 529.86 13471
5. PRESENT RESEARCH WORK In view of the above, there is an ongoing research on Modelling and Recognition of Faults in Smart Distribution Grid Using Machine Intelligent Techniques. Following the Design Science Research (DSR) approach, a smart distribution network system will be designed and simulated in this study as an artefact. Faults will be simulated in a test system and the faults will be recognized and located using machine intelligent algorithms (which are also artefacts). The effectiveness and accuracy of the fault recognition algorithms will be determined using appropriate evaluation metrics. Fig. 1 shows an existing system architecture for automated fault analysis in electrical power systems [52] This study will follow the architecture and similar ones in the literature to implement Web Services for Automated Fault Analysis in Power Systems (WS-AFA). This will form an additional DSR artefact for performing online fault and disturbance visualization via the Internet. Historical data (real-time or simulated) of different types of faults on the distribution network of Ethekwini Electricity will be trained on machine learning models, making it possible for the models to identify and locate similar faults when they occur. 6. CONCLUSION
This paper has presented a review of several research works in the area of identification, diagnosis and location of faults in smart distribution grids. There have been intensified efforts over the years in realizing these objectives and a lot of progress has been made but there are still rooms for improvements. In general, there is no one single best method for all situations [19]. Machine Intelligent Techniques (MIT) has also been proposed for fault recognition in the ongoing research work. Future research studies should endeavor to develop improved methods with both technical and economic considerations.
Fig. 1 System Architecture for Automated Fault Analysis in Electrical Power Systems [53]
7. REFERENCES [1]
[2] [3] [4] [5]
[6]
[7]
H. G. Jiang, J. J. Zhang, W. Z. Gao, and Z. P. Wu, "Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods," Ieee Transactions on Smart Grid, vol. 5, pp. 2947-2956, Nov 2014. M. Wissner, "The Smart Grid–A saucerful of secrets?," Applied Energy, vol. 88, pp. 2509-2518, 2011. H.-Y. Wei, J.-B. Lan, Y.-F. Ye, X.-H. Li, and J.-F. Yang, "Self-healing Control Algorithms for Distribution Networks," pp. 502 - 507, 2015. F. C. Trindade, W. Freitas, and J. C. Vieira, "Fault location in distribution systems based on smart feeder meters," IEEE Transactions on Power Delivery, vol. 29, pp. 251-260, 2014. H. Jiang, J. J. Zhang, W. Gao, and Z. Wu, "Fault detection, identification, and location in smart grid based on data-driven computational methods," IEEE Transactions on Smart Grid, vol. 5, pp. 29472956, 2014. C. P. Nwokolo, "Standardization of Education in Nigeria through Information Communications Technology (ICT)," Journal of Educational Policy and Entrepreneurial Research, vol. 2, pp. 38-49, 2015. T. W. A. Nugroho, "ANALISIS E-GOVERNMENT TERHADAP PELAYANAN PUBLIK DI KEMENTERIAN HUKUM DAN HAM (Analysis Of E-Government To Public Services In The Ministry Of Law And Human Rights)," Jurnal Ilmiah Kebijakan Hukum, vol. 10, pp. 279-296, 2017.
[8] [9] [10] [11] [12]
[13]
[14]
[15] [16]
[17]
[18] [19]
[20]
[21]
[22] [23]
[24] [25]
[26]
[27] [28] [29]
A. W. Blackett, M. E. Teachman, and B. J. Forth, "Communications architecture for intelligent electronic devices," ed: Google Patents, 2005. V. Calderaro, C. N. Hadjicostis, A. Piccolo, and P. Siano, "Failure identification in smart grids based on petri net modeling," IEEE Transactions on Industrial Electronics, vol. 58, pp. 4613-4623, 2011. S. F. Bush, "Network theory and smart grid distribution automation," IEEE Journal on Selected Areas in Communications, vol. 32, pp. 1451-1459, 2014. S. F. Bush, S. Goel, and G. Simard, "IEEE vision for smart grid communications: 2030 and beyond roadmap," 2013. Z. Sumic and R. Vidyanand, "Fuzzy set theory based outage determination [for distribution networks]," in Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP'96., International Conference on, 1996, pp. 204-208. D. Srinivasan, R. L. Cheu, Y. P. Poh, and A. K. C. Ng, "Automated fault detection in power distribution networks using a hybrid fuzzy–genetic algorithm approach," Engineering Applications of Artificial Intelligence, vol. 13, pp. 407-418, 2000. J. Aguero and A. Vargas, "Inference of operative configuration of distribution networks using fuzzy logic techniques-Part I: real-time model," IEEE Transactions on Power Systems, vol. 20, pp. 15511561, 2005. Y. Liu and N. N. Schulz, "Knowledge-based system for distribution system outage locating using comprehensive information," IEEE Transactions on Power Systems, vol. 17, pp. 451-456, 2002. Y. Zhao, R. Sevlian, R. Rajagopal, A. Goldsmith, and H. V. Poor, "Outage detection in power distribution networks with optimally-deployed power flow sensors," in Proc. of IEEE Power and Energy Society General Meeting, 2013. S. T. Mak and N. Farah, "Synchronizing SCADA and smart meters operation for advanced smart distribution grid applications," in Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, 2012, pp. 1-7. K. B. Konstantinov and T. Yoshida, "Real‐time qualitative analysis of the temporal shapes of (bio) process variables," AIChE Journal, vol. 38, pp. 1703-1715, 1992. N. Ruiz-Reyes, P. Vera-Candeas, and F. Jurado, "Discrimination between transient voltage stability and voltage sag using damped sinusoids-based transient modeling," IEEE Transactions on Power Delivery, vol. 20, pp. 2644-2650, 2005. B. Bakshi and G. Stephanopoulos, "Representation of process trends—IV. Induction of real-time patterns from operating data for diagnosis and supervisory control," Computers & Chemical Engineering, vol. 18, pp. 303-332, 1994. R. Rengaswamy and V. Venkatasubramanian, "A syntactic pattern-recognition approach for process monitoring and fault diagnosis," Engineering Applications of Artificial Intelligence, vol. 8, pp. 35-51, 1995. W. Sun, A. Palazoğlu, and J. A. Romagnoli, "Detecting abnormal process trends by wavelet‐domain hidden Markov models," AIChE Journal, vol. 49, pp. 140-150, 2003. R. Srinivasan and M. Qian, "Online monitoring of process states using dynamic programming and dynamic time warping," in AIChE Annual Meeting, 263g, Indianapolis, IN, November, 2002, pp. 38. J.-Y. Cheung and G. Stephanopoulos, "Representation of process trends—Part I. A formal representation framework," Computers & Chemical Engineering, vol. 14, pp. 495-510, 1990. M. E. Janusz and V. Venkatasubramanian, "Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis," Engineering Applications of Artificial Intelligence, vol. 4, pp. 329-339, 1991. R. A. F. Pereira, L. G. W. da Silva, M. Kezunovic, and J. R. S. Mantovani, "Improved fault location on distribution feeders based on matching during-fault voltage sags," IEEE Transactions on Power Delivery, vol. 24, pp. 852-862, 2009. M. Kezunovic, "Smart fault location for smart grids," IEEE transactions on smart grid, vol. 2, pp. 1122, 2011. J. E. Tate and T. J. Overbye, "Line outage detection using phasor angle measurements," IEEE Transactions on Power Systems, vol. 23, pp. 1644-1652, 2008. J. E. Tate and T. J. Overbye, "Double line outage detection using phasor angle measurements," in 2009 IEEE Power & Energy Society General Meeting, 2009, pp. 1-5.
[30] [31] [32] [33] [34] [35] [36] [37]
[38] [39]
[40]
[41]
[42] [43] [44]
[45]
[46]
[47] [48] [49] [50] [51] [52] [53]
M. Chertkov, F. Pan, and M. G. Stepanov, "Predicting failures in power grids: The case of static overloads," IEEE Transactions on Smart Grid, vol. 2, pp. 162-172, 2011. Y. Cai, M.-Y. Chow, W. Lu, and L. Li, "Statistical feature selection from massive data in distribution fault diagnosis," IEEE Transactions on Power Systems, vol. 25, pp. 642-648, 2010. R. Aggarwal and Y. Song, "Artificial neural networks in power systems. III. Examples of applications in power systems," Power Engineering Journal, vol. 12, pp. 279-287, 1998. P. G. Axelberg, I. Y.-H. Gu, and M. H. Bollen, "Support vector machine for classification of voltage disturbances," IEEE Transactions on Power Delivery, vol. 22, pp. 1297-1303, 2007. Y.-C. Huang, "Fault section estimation in power systems using a novel decision support system," IEEE Transactions on power systems, vol. 17, pp. 439-444, 2002. C.-F. Chien, S.-L. Chen, and Y.-S. Lin, "Using Bayesian network for fault location on distribution feeder," IEEE Transactions on Power Delivery, vol. 17, pp. 785-793, 2002. Y. M. Park, G.-W. Kim, and J.-M. Sohn, "A logic based expert system (LBES) for fault diagnosis of power system," IEEE Transactions on Power systems, vol. 12, pp. 363-369, 1997. C. Lo, Y. Wong, and A. B. Rad, "Intelligent system for process supervision and fault diagnosis in dynamic physical systems," IEEE Transactions on Industrial Electronics, vol. 53, pp. 581-592, 2006. S. A. Arogeti, D. Wang, and C. B. Low, "Mode identification of hybrid systems in the presence of fault," IEEE Transactions on Industrial Electronics, vol. 57, pp. 1452-1467, 2010. B. Lu and V. C. Gungor, "Online and remote motor energy monitoring and fault diagnostics using wireless sensor networks," IEEE Transactions on Industrial Electronics, vol. 56, pp. 4651-4659, 2009. N. I. Elkalashy, M. Lehtonen, H. A. Darwish, A.-M. I. Taalab, and M. A. Izzularab, "DWT-based detection and transient power direction-based location of high-impedance faults due to leaning trees in unearthed MV networks," IEEE Transactions on Power Delivery, vol. 23, pp. 94-101, 2008. N. I. Elkalashy, A. M. Elhaffar, T. A. Kawady, N. G. Tarhuni, and M. Lehtonen, "Bayesian selectivity technique for earth fault protection in medium-voltage networks," IEEE Transactions on Power Delivery, vol. 25, pp. 2234-2245, 2010. S. Balser, K. Clements, and D. Lawrence, "A microprocessor-based technique for detection of high impedance faults," IEEE Transactions on Power Delivery, vol. 1, pp. 252-258, 1986. J. Zhu, D. L. Lubkeman, and A. A. Girgis, "Automated fault location and diagnosis on electric power distribution feeders," IEEE Transactions on Power Delivery, vol. 12, pp. 801-809, 1997. C. Fukui and J. Kawakami, "An expert system for fault section estimation using information from protective relays and circuit breakers," IEEE Transactions on Power Delivery, vol. 1, pp. 83-90, 1986. C. Xu, Z. Zhong, V. Centeno, R. Conners, and Y. Liu, "Practical issues in frequency disturbance recorder design for wide-area monitoring," Electrical Power Quality and Utilisation. Journal, vol. 11, pp. 69-76, 2005. M. He and J. Zhang, "Fault detection and localization in smart grid: A probabilistic dependence graph approach," in Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, 2010, pp. 43-48. M. He and J. Zhang, "A dependency graph approach for fault detection and localization towards secure smart grid," IEEE Transactions on Smart Grid, vol. 2, pp. 342-351, 2011. W. Gao and J. Ning, "Wavelet-based disturbance analysis for power system wide-area monitoring," IEEE Transactions on Smart Grid, vol. 2, pp. 121-130, 2011. J. Ning, J. Wang, W. Gao, and C. Liu, "A wavelet-based data compression technique for smart grid," IEEE Transactions on Smart Grid, vol. 2, pp. 212-218, 2011. R. A. Len, V. Vittal, and G. Manimaran, "Application of sensor network for secure electric energy infrastructure," IEEE Transactions on Power Delivery, vol. 22, pp. 1021-1028, 2007. S. Kazemi, M. Lehtonen, and M. Fotuhi-Firuzabad, "Impacts of fault diagnosis schemes on distribution system reliability," IEEE Transactions on Smart Grid, vol. 3, pp. 720-727, 2012. R. J. Patton, P. M. Frank, and R. N. Clark, Issues of fault diagnosis for dynamic systems: Springer Science & Business Media, 2013. M. Musaruddin and R. Zivanovic, "Web services for automated fault analysis in electrical power system," in INC, IMS and IDC, 2009. NCM'09. Fifth International Joint Conference on, 2009, pp. 829-832.
