Hybrid signal processing and machine learning algorithm for adaptive fault classification of wind farm integrated transmision line protection

The technological advancement in integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photovoltaic (PV) system into conventional power system as a future solution to meet the increase in global energy demands in order to reduce the cost of power generation, and improve on the climate change impact. This innovation also introduces challenges in the power system protection by it being compromised due to injected fault current infeeds on existing facilities. These infeed leadto the undesired trip of a healthy section of the line, and protection system failure. This paper presents a soft computational approach to adaptive fault classification model on High VoltageTransmission Line (HVTL) with and without RGES-WFG integration topologies, using extracted one-cycle fault signature of voltage and current signals with wavelet statistical approach in Matlab. The results are unique signatures across all fault types and fault distances with distinct entropy energy values on proposed network architecture. The supervised machine learning algorithm from Bayesian network classified 99.15 % faults correctly with the operation time of 0.01 s to produced best-generalized model with an RMS error value of 0.05 for single line-to-ground (SLG) fault identification and classification. Best suitable for adaptive unit protection scheme integration

Faults signature extraction in wind farm integrated Transmission Line topology

The integration of Renewable Green Energy Sources (RGES) like Wind Farm Generators (WFG), and Photo Voltaic (PV) systems into convention power system as a future solution to the increase in global energy demands, generation cost reduction, and limited climate impact. The innovation introduced protection compromise challenges in power system due to in-feeds fault current penetration from RGES on existing system, leading to an undesired trip of the healthy section of TL, equipment damages, and safety failure. A comparison study of extracted faults signature from two proposed Transmission Line (TL) network topologies with and without WFG integration, for onward fault identification, and classification model design. Descrete wavelet multiresolution Analysis (DWMRA) of extracted one-cycle fault signal signatures from 11 faults type’s scenarios in Matlab. Result demonstrated a unique fault signatures across all simulated faults scenarios harness for future work of an adaptive unit protection model for this new area of DG integration

Improvement on optimal coordination of directional overcurrent relays in mesh distribution network system using artificial intelligence technique

The electrical meshed distribution network (MDN) protection coordination scheme,poses great challenges to protection coordination scheme setup, due to the network topology structure. This always resulted to unexpected miscoordination among selected primary and backup relay pairs due to multi-directional fault current infeed’s contributions by all interconnected electrical power sources to the short circuit fault current magnitude level. Moreover, other challenges to be addressed is in the ineffective prediction of the nonlinear time-current characteristic function curve from empirical data as earlier proposed in previous research, for the future determination of the relay operation time response to short circuit fault in other locations. This research work propose the artificial intelligent (AI) solution on the conventional objective function (COF) and the modified objective function (MOF) formulation, with the application of genetic algorithm (GA) optimization solver, to determine each relay best optimal operation parameters selection for the time dial settings (TDS), plug setting (PS) and response time to fault accordingly. Also, the elimination of pending miscoordination amongst relay pair for effective coordination scheme. Furthermore, a novel hybrid GAANN technique is proposed for the supervised training, to predict the nonlinear timecurrent characteristic function fitting of each relay operation time function. A directional overcurrent relay (DOCR) coordination in IEEE 9 bus test system is proposed for this research work with three integrated multi distribution generation electrical power sources (DG) in DigSiLent power factory and Matlab Simulink software. The obtained result from the GA solution of the MOF produced a 91.67% improvement in the obtained optimal parameter values against the 8.33% reduced value from COF. This also translated into the same percentage values in operation time response to fault within each relay protection coverage zones. Furthermore, the pending miscoordination amongst selected relay pairs of 16.67% earlier experienced in COF solution is been eliminated by the GA solution of the MOF with 100% elimination between the selected primary and backup relay pairs. This is substantiated by the lower fitness mean value of 1.3358 from MOF against the 4.7679 from the COF for the same minimization problem. However,the Levenberg–Marquardt nonlinear function fitting algorithm application on solving the novel hybrid GA-ANN technique predicted the nonlinear time –current function fitting of each relay effectively with minimum mean square error (mse) between the target output and the actual output for effective generalization during supervised training of the network. This research work has achieved all proposed objective function by improving and eliminating all pending problem encountered in multi sources MDN

Development of a modified adaptive protection scheme using machine learning technique for fault classification in renewable energy penetrated transmission line

The conventional utility grid-protection scheme is predesigned at the network's early planning stage with consideration to the high short circuit fault current magnitude contribution level from the Synchronous Generators (SG) to prevent the mal-operation of the relaying scheme. In the modern power system grid, the integration of the Renewable Energy Resources (RER) from Windfarms (WF) or Photovoltaic (PV) generation sources focused on addressing the climate change environmental issues and solving the impending future energy sustainability challenges. In compliance with the new grid code requirement of permanently integrating RER with the conventional SG sources during grid short circuit faults, also known as the low voltage fault ride-through (LVFRT). Such RERs integration phenomenon compromised the existing protection relaying scheme operation settings due to the power grid system topology changes. The added infeed current penetration from integrated RERs impacted adversely on the existing protective relay operation setting compromise. The relay operation setting compromise is due to the wrong estimated impedance seen by the relay leading to overreach or underreach mal-operation. The current Adaptive Protection Scheme (APS) motivation focused on the accurate relay operation setting changes based on the prevailing grid system configuration variations. Hence, eliminate the utility grid relay operation setting compromise. The lack of healthy lines detailed protection useful information knowledge has limited the existing APS performance, as only faulty lines' measured parameters (voltage, current, and phase angle) are mostly used in the relaying protection scheme design. The high-cost of implementations, cyber-attack, and latency concerns from the adopted communication channels for the standard APS relay characteristic setting and selection is another drawback identified. This study proposed a modified standalone Machine Learning-based Adaptive Protection Scheme (ML-APS) relay' fault classifier model using novel useful hidden Knowledge Discovery from historical fault events Dataset (KDD) from healthy and faulty lines. The healthy lines extracted fault signals' functional signature are added to the earlier deployed faulty-line decomposed dataset, operation parameters, and changing network topology information from the SCADA logged reports without communication channel use. The hybrid Wavelet Multiresolution Analysis and Machine learning algorithm (WMRA-ML) is used to extracts the useful hidden knowledge from decomposed one-cycle fault transient signals (voltage & current) from four Matlab/Simulink CIGRE models. Consideration was given to different RER penetration levels based on the changing network topologies subjected to twelve different short circuit fault scenarios.The selected 29 unique feature attributes across 15,120 historical faults dataset deployed as the input-output training dataset for the ML-APS relay classifier model development in Waikato Environment of Knowledge Analysis Software (WEKA). The obtained result from the twelve deployed ML algorithms for the standalone intelligent ML-APS relay classifier modification without communication medium adoption for transmitting and receiving the updated relay operation settings during network configuration changes. The Random Tree standalone ML-AP relay model presented the best performing models from the ML-APS relay model with the best average performance for the correctly classified fault types of 97.61 % at 5 % significance level above other ML algorithms. The recorded kappa statistic value of 0.9802, and the Receiver Operating Curve (ROC) area of 98.73 %. The Random Tree relay algorithm model presented an improved average trip decision time of 18 ms compared with the standard minimum value of 20 ms recorded for the conventional relay due to eliminated communication channels. The ML-AP relay model addressed the cyber-attack and latency compromises in the earlier APS relay for the modern power system network. The obtained result demonstrated useful hidden knowledge in the healthy line sections that have contributed valuable information for improved ML-APS relay model for the faults detection, discrimination, and decision trip improvement during the grid short circuit faults

Wind power plants protection using overcurrent relays

Wind power plants are one of the most crucial types of renewable energies which are increasingly employed in smart grids with purpose of power generation especially as a distribution generation system. Hence, the proper protection of wind plants is an enormously significant aspect which must be taken into consideration when designing the wind plants to not only provide a suitable protection for the power components but also maintain the power generation perpetually in case of fault. The most important and common protection systems are overcurrent relays which can protect the power systems from impending faults. In order to implement a successful and proper protection for wind power plants, these relays must be set accurately and well coordinated with each other to clear the fault at the system in the shortest possible time. This paper demonstrates how the coordination of overcurrent relays can be successfully achieved in wind power plants in order to maintain the power generation during fault and protect the power components