Online coherency identification and stability condition for large interconnected power systems using an unsupervised data mining technique

Identification of coherent generators and the determination of the stability system condition in large interconnected power system is one of the key steps to carry out different control system strategies to avoid a partial or complete blackout of a power system. However, the oscillatory trends, the larger amount data available and the non-linear dynamic behaviour of the frequency measurements often mislead the appropriate knowledge of the actual coherent groups, making wide-area coherency monitoring a challenging task. This paper presents a novel online unsupervised data mining technique to identify coherent groups, to detect the power system disturbance event and determine status stability condition of the system. The innovative part of the proposed approach resides on combining traditional plain algorithms such as singular value decomposition (SVD) and K -means for clustering together with new concept based on clustering slopes. The proposed combination provides an added value to other applications relying on similar algorithms available in the literature. To validate the effectiveness of the proposed method, two case studies are presented, where data is extracted from the large and comprehensive initial dynamic model of ENTSO-E and the results compared to other alternative methods available in the literature

PMU-Based ROCOF Measurements: Uncertainty Limits and Metrological Significance in Power System Applications

In modern power systems, the Rate-of-Change-of-Frequency (ROCOF) may be largely employed in Wide Area Monitoring, Protection and Control (WAMPAC) applications. However, a standard approach towards ROCOF measurements is still missing. In this paper, we investigate the feasibility of Phasor Measurement Units (PMUs) deployment in ROCOF-based applications, with a specific focus on Under-Frequency Load-Shedding (UFLS). For this analysis, we select three state-of-the-art window-based synchrophasor estimation algorithms and compare different signal models, ROCOF estimation techniques and window lengths in datasets inspired by real-world acquisitions. In this sense, we are able to carry out a sensitivity analysis of the behavior of a PMU-based UFLS control scheme. Based on the proposed results, PMUs prove to be accurate ROCOF meters, as long as the harmonic and inter-harmonic distortion within the measurement pass-bandwidth is scarce. In the presence of transient events, the synchrophasor model looses its appropriateness as the signal energy spreads over the entire spectrum and cannot be approximated as a sequence of narrow-band components. Finally, we validate the actual feasibility of PMU-based UFLS in a real-time simulated scenario where we compare two different ROCOF estimation techniques with a frequency-based control scheme and we show their impact on the successful grid restoration.Comment: Manuscript IM-18-20133R. Accepted for publication on IEEE Transactions on Instrumentation and Measurement (acceptance date: 9 March 2019

The effect of inrush transients on pv inverter's grid impedance measurement based on inter-harmonic injection

This paper addresses a cause for false tripping of photovoltaic inverters with antiislanding protection based on impedance measurement with inter-harmonic injection. Earlier discussions about tripping problems happening when several devices are doing the measurement at the same time are supplemented with a problem caused by inrush transients of nearby devices. A series of experiments was conducted in the Power Quality laboratory of the TU/e, on a PV inverter which complies with the DIN VDE 0126 standard. Impedance measurement was done in parallel with the inverter and measurement results are presented. A criterion for false tripping caused by transients is explored. Also, influences of network impedance and grid harmonic pollution on false tripping were analyzed. In the end, some signal processing techniques are proposed to avoid this problem

Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Detection and Discrimination of Islanding and Faults in distribution system with Distributed Generation by using Wavelet based Alienation approach

This paper presents a wavelet transform based alienation technique for the protection of a radial 5 bus  distribution system integrated with four wind type doubly fed Induction generators (DFIG).This technique is used to detect islanding condition and faults, classification of faults and their discrimination. Islanding is simulated at point of common coupling (PCC) and faults are simulated at each DG bus of the network. Daubechies wavelet transform has been used to decompose the current signals to get approximate coefficients. The Alienation coefficients of these approximate decompositions are termed as islanding and fault indexes. These indexes have been compared with predetermined threshold to detect islanding and faults. The same threshold value is utilized to discriminate transients associated with islanding and fault. Alienation coefficients at each bus over a half cycle window clearly detect both islanding and fault. Testing of the proposed algorithm has been carried out for various angles of incidence. Hence, the proposed algorithm is more effective and successful for finding the islanding condition as well as faults in distribution system with distributed generation

Islanding Detection in Micro-grids using Sum of Voltage and Current Wavelet Coefficients Energy before the Main Circuit Breaker Side

This paper presents wavelet based islanding detection in distributed generation (DG) interfaced to the microgrid. Also a new fast method is developed for islanding detection based on measuring the utility currents and voltages signals processed by discrete wavelet transform. These currents and voltages signals are measured before the main circuit breaker of microgrid network and their features extracted by discrete wavelet transform. These features are sum of wavelet coefficients energy and are used for distinguishing the islanding conditions from non-islanding ones. Because of changing in measuring point of currents and voltages signals from point of common coupling (PCC) in traditional methods to before the main circuit breaker in proposed method, this new method detects the islanding conditions faster than the other methods. The proposed method has been examined under various scenarios; including mains supply faults, various one, two, or three phases' grid faults, and changes of rate of produced energy on IEEE 1547 anti-islanding test system. The numerical studies show the feasibility and applicability of the proposed method with satisfactory results

Peak-ratio analysis method for enhancement of LOM protection using M class PMUs

A novel technique for loss of mains (LOM) detection, using Phasor Measurement Unit (PMU) data, is described in this paper. The technique, known as the Peak Ratio Analysis Method (PRAM), improves both sensitivity and stability of LOM protection when compared to prevailing techniques. The technique is based on a Rate of Change of Frequency (ROCOF) measurement from M-class PMUs, but the key novelty of the method lies in the fact that it employs a new “peak-ratio” analysis of the measured ROCOF waveform during any frequency disturbance to determine whether the potentially-islanded element of the network is grid connected or not. The proposed technique is described and several examples of its operation are compared with three competing LOM protection methods that have all been widely used by industry and/or reported in the literature: standard ROCOF, Phase Offset Relay (POR) and Phase Angle Difference (PAD) methods. It is shown that the PRAM technique exhibits comparable performance to the others, and in many cases improves upon their abilities, in particular for systems where the inertia of the main power system is reduced, which may arise in future systems with increased penetrations of renewable generation and HVDC infeed

Communication based loss-of-mains protection method by frequency correlation

Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms.Due to the increasing penetration of distributed generation (DGs) in the distribution network in high numbers and proportions, and its conspicuous impact on power system stability. This occurs during a wide system disturbance in the power system, the DGs will start to disconnect from the main source in large proportions. This will further affect the power system stability and causes damages to its components and DGs. This thesis investigates in the reliability, security, and efficiency of satellite and internet communications, specifically for loss of mains (LOM) protection and exploring the strengths, the weaknesses, the feasibility of each type of communications, and the requirements of communication system components. By using communications network to send Phasor Measurement Unit (PMU) data to DGs protection equipment that are connected at remote areas all over UK, the LOM protection can be improved, obtain synchronization, precision, and coordination among power protection components. Satellite communication is chosen as it makes a better communication method when it comes to the installation, construction, urban disruption, time saving, and the installation and annual cost on every participant. However, the high latency issue is approached and solved by making a few changes in the communication protocol format and the data requirements to reduce the effect of latency to a level that can be tolerated. This thesis presents the development of a novel LOM protection method based on communication and frequency correlation. The stability and sensitivity assessment will show that this method is highly secure and reliable. It can also withstand a communication delay of 120ms without causing any nuisance tripping, and have a relay response to LOM operation of a maximum of 1s. The thesis also presents a novel method in time delay estimation that has been developed for power system applications. This method is called the Linear Trajectory Path (LTP) and its performance fulfils the LOM synchronisation requirements by succeeding in determining the time delay between the two data streams within the tolerated estimation error of ±100ms

AN INTELLIGENT PASSIVE ISLANDING DETECTION AND CLASSIFICATION SCHEME FOR A RADIAL DISTRIBUTION SYSTEM

Distributed generation (DG) provides users with a dependable and cost-effective source of electricity. These are directly connected to the distribution system at customer load locations. Integration of DG units into an existing system has significantly high importance due to its innumerable advantages. The high penetration level of distributed generation (DG) provides vast techno-economic and environmental benefits, such as high reliability, reduced total system losses, efficiency, low capital cost, abundant in nature, and low carbon emissions. However, one of the most challenges in microgrids (MG) is the island mode operations of DGs. the effective detection of islanding and rapid DG disconnection is essential to prevent safety problems and equipment damage. The most prevalent islanding protection scheme is based on passive techniques that cause no disruption to the system but have extensive non-detection zones. As a result, the thesis tries to design a simple and effective intelligent passive islanding detection approach using a CatBoost classifier, as well as features collected from three-phase voltages and instantaneous power per phase visible at the DG terminal. This approach enables initial features to be extracted using the Gabor transform (GT) technique. This signal processing (SP) technique illustrates the time-frequency representation of the signal, revealing several hidden features of the processed signals to be the input of the intelligent classifier. A radial distribution system with two DG units was utilized to evaluate the effectiveness of the proposed islanding detection method. The effectiveness of the proposed islanding detection method was verified by comparing its results to those of other methods that use a random forest (RF) or a basic artificial neural network (ANN) as a classifier. This was accomplished through extensive simulations using the DigSILENT Power Factory® software. Several measures are available, including accuracy (F1 Score), the area under the curve (AUC), and training time. The suggested technique has a classification accuracy of 97.1 per cent for both islanded and non-islanded events. However, the RF and ANN classifiers\u27 accuracies for islanding and non-islanding events, respectively, are proven to be 94.23 and 54.8 per cent, respectively. In terms of the training time, the ANN, RF, and CatBoost classifiers have training times of 1.4 seconds, 1.21 seconds, and 0.88 seconds, respectively. The detection time for all methods was less than one cycle. These metrics demonstrate that the suggested strategy is robust and capable of distinguishing between the islanding event and other system disruptions

A New Islanding Detection Method Based On Wavelet-transform and ANN for Inverter Assisted Distributed Generator

Nowadays islanding has become a big issue with the increasing use of distributed generators in power system. In order to effectively detect islanding after DG disconnects from main source, author first studied two passive islanding methods in this thesis: THD&VU method and wavelet-transform method. Compared with other passive methods, each of them has small non-detection zone, but both of them are based on the threshold limit, which is very hard to set. What’s more, when these two methods were applied to practical signals distorted with noise, they performed worse than anticipated. Thus, a new composite intelligent based method is presented in this thesis to solve the drawbacks above. The proposed method first uses wavelet-transform to detect the occurrence of events (including islanding and non-islanding) due to its sensitivity of sudden change. Then this approach utilizes artificial neural network (ANN) to classify islanding and non-islanding events. In this process, three features based on THD&VU are extracted as the input of ANN classifier. The performance of proposed method was tested on two typical distribution networks. The obtained results of two cases indicated the developed method can effectively detect islanding with low misclassification