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PL
Wielkoobszarowe systemy pomiarowe (WAMS – Wide Area Measurement Systems) zyskują obecnie na znaczeniu w pracującym synchronicznie, połączonym – europejskim systemie elektroenergetycznym, w którym obserwuje się rosnące obciążenie istniejącej sieci, coraz więcej obiektów wykorzystujące konwertery i silniejsze przepływy transgraniczne czy międzyregionalne. Potencjał, jaki przynoszą zsynchronizowane czasowo pomiary z PMU (Phasor Measurement Unit), ujawnia się w możliwości identyfikacji nieklasycznych zakłóceń, dla których systemy SCADA mogą być niewystarczające do wykrycia ich zaistnienia, m.in. z powodu niedostatecznej rozdzielczości pomiarowej i/lub lokalnej realizacji pomiarów. Pomiary synchrofazorowe coraz częściej służą operatorom do realizacji nowych funkcji w zakresie m.in.: monitorowania, sterowania, zabezpieczeń i analiz off-line z wykorzystaniem zsynchronizowanych danych pomiarowych. Stan obecnego wykorzystania technologii PMU w Europie i na świecie wraz z innowacyjnymi kierunkami dotyczącymi nowych możliwości ich wykorzystania, zostanie przedstawiony w niniejszym artykule, wraz ze wskazaniem planów rozwoju WAMS w KSE.
EN
Wide Area Measurement Systems (WAMS) are now gaining more and more importance in the synchronously interconnected European power system with increasing load on the existing grid, more converter-based facilities and stronger cross-border or inter-regional flows. The potential of time-synchronized measurements provided by the PMUs (Phasor Measurement Units) is revealed in the possibility of identifying non-classical disturbances for which SCADA may be insufficient to detect their occurrence, e.g. due to insufficient measurements resolution and/or their local implementation. Synchorphasor measurements are more frequently used by operators to introduce new functions for the sake of e.g. monitoring, control, protection and off-line analysis based on the synchronized measurement data. The state of the current use of PMU technology in Europe and in the world, along with innovative directions for new possibilities of their use, will be presented in this article, as well as WAMS development plans in the Polish Power System.
EN
The penetration of renewable energy sources into the electricity distribution network causes changes in power flows, making conventional protection no longer effective. Research suggests that the solution should be the massive deployment of phasor measurement units and a centralised control, monitoring and protection system. But is this system sufficiently ready for widespread deployment in practice? While research on the decentralised system is progressing further while linking it to microgrid applications is less common.
PL
Wnikanie odnawialnych źródeł energii do sieci dystrybucyjnej energii elektrycznej powoduje zmiany w przepływach mocy, sprawiając, że konwencjonalne zabezpieczenia przestają być skuteczne. Badania sugerują, że rozwiązaniem powinno być masowe rozmieszczenie jednostek pomiaru fazorów oraz scentralizowanego systemu kontroli, monitorowania i ochrony. Ale czy ten system jest wystarczająco gotowy do szerokiego zastosowania w praktyce? Podczas gdy badania nad systemem zdecentralizowanym postępują dalej, łączenie go z aplikacjami mikrosieciowymi jest mniej powszechne.
EN
The distance relay using the transmission line impedance measurement identifies the type and location of the fault. However, any other factors that cause the failure of the measured impedance, makes the relay detect the fault in incorrect location or do not detect the fault at all. One of these factors is the fault resistance which directly increases the measured impedance by the relay. Another factor that indirectly alters the impedance of the transmission line is static synchronous compensator (STATCOM). When a fault happens, current injection by the STATCOM changes the measured signals by the relay and thus makes the calculated impedance incorrect. In this paper, a method is provided based on the combination of distance and differential protection. Firstly, from the current data of buses, faulted transmission line is detected. Then using the presented algorithm, the fault location is calculated on the transmission line. The basis of the algorithm is on the active power calculation of the buses. Fault resistance is calculated from the active powers and its effect will be deducted from calculated impedance by the algorithm. Furthermore, with choosing data of appropriate bus, STATCOM effect is eliminated, and fault location will be obtained.
EN
The electrical network is a man-made complex network that makes it difficult to monitor and control the power system with traditional monitoring devices. Traditional devices have some limitations in real-time synchronization monitoring which leads to unwanted behavior and causes new challenges in the operation and control of the power systems. A Phasor measurement unit (PMU) is an advanced metering device that provides an accurate real-time and synchronized measurement of the voltage and current waveforms of the buses in which the PMU devices are directly connected in the grid station. The device is connected to the busbars of the power grid in the electrical distribution and transmission systems and provides time-synchronized measurement with the help of the Global Positioning System (GPS). However, the implementation and maintenance cost of the device is not bearable for the electrical utilities. Therefore, in recent work, many optimization approaches have been developed to overcome optimal placement of PMU problems to reduce the overall cost by providing complete electrical network observability with a minimal number of PMUs. This research paper reviews the importance of PMU for the modern electrical power system, the architecture of PMU, the differences between PMU, micro-PMU, SCADA, and smart grid (SG) relation with PMU, the sinusoidal waveform, and its phasor representation, and finally a list of PMU applications. The applications of PMU are widely involved in the operation of power systems ranging from power system control and monitor, distribution grid control, load shedding control and analyses, and state estimation which shows the importance of PMU for the modern world.
EN
Unintentional islanding detection is one the mandatory criterion that must be met by PV inverters before connecting them into the grid. Acceptable time for inverter for islanding detection is less than 2 seconds. In this paper voltage parameters after islanding occurrence and before turning off the inverter are analyzed. In order to simulate islanding state and perform measurements the testing system was build. Three different commercial PV inverters were tested. Measured signals were used to calculate voltage envelope, phasor, frequency and ROCOF. Collected data proved to be helpful to compere different inverters.
EN
In distribution networks, PMU (Phasor Measurement Unit) is required for each node or bus, but the cost of installing PMU is quite expensive, so optimization of PMU placement is required. This study uses the Integer Linear K-means Clustering method and uses the parameters of voltage, current and impedance. This method is a combination of Linear Integration and K-Means methods used for optimizing PMU placement. The object used for research is the Bendul-Merisi distribution network which has 11 buses. The results showed that the Integer Linear K-means Clustering method can be used for PMU placement optimization. With a network of 11 buses, only 3 PMU is needed, resulting in a reduction in the number of PMU by 73%.
PL
W artykule zaproponowano metodę optymalizacji rozmieszczenia w sieci układów do pomiaru fazy. Zastosowano kombinację metod: Linear Integration I K-means. Na przykładzie sieci w Bendul Merisi (Indonezja) zredukowano liczbę mierników o 73%.
EN
Power quality (PQ) monitoring is important for both the utilities and also the users of electric power. The most widespread measurement instrument used for PQ monitoring is the PQM (Power Quality Monitor) or PQA (Power Quality Analyzer). In this paper we propose the usage of PMU data for PQ parameters monitoring. We present a new methodology of PQ parameters monitoring and classification based on PMU data. The proposed methodology is tested with real measurements performed in distribution system using dedicated PMU system.
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