Localisation of partial discharge sources using radio fingerprinting technique

Partial discharge (PD) is a well-known indicator of the failure of insulators in electrical plant. Operators are pushing toward lower operating cost and higher reliability and this stimulates a demand for a diagnostic system capable of accurately locating PD sources especially in ageing electricity substations. Existing techniques used for PD source localisation can be prohibitively expensive. In this paper, a cost-effective radio fingerprinting technique is proposed. This technique uses the Received Signal Strength (RSS) extracted from PD measurements gathered using RF sensors. The proposed technique models the complex spatial characteristics of the radio environment, and uses this model for accurate PD localisation. Two models were developed and compared: k-nearest neighbour and a feed-forward neural network which uses regression as a form of function approximation. The results demonstrate that the neural network produced superior performance as a result of its robustness against noise

RSS Based Localization for Partial Discharge Source Using Received Signal Strength Only

Partial discharge (PD) localization has been performed on a periodic or on a request basis to assess the health of high-voltage (HV) systems mainly due to lack of feasibility of techniques for continuous monitoring and localization. Advancements in the field of communication technology have made it possible to detect and locate PD activity in HV systems on a continuous basis. Existing PD localization techniques mainly include the time of arrival (TOA), time difference of arrival (TDOA) and angle of arrival (AOA) methods. These techniques require time-based synchronization of sensor nodes that are involved in the receiver system resulting in expensive and complex hardware and software solutions. In this thesis, a received signal strength (RSS) based localization of PD is proposed. It is demonstrated that RSS based localization can be used under anonymous and harsh industrial environments for PD localization. RSS based localization does not require synchronization because unlike TOA, TDOA and AOA, it processes the amplitude of the received signal and not its phase. A theoretical model of the algorithm has been developed based on the path loss model equation. Simulations have been performed to prove the principle in noiseless and noisy conditions before the experimental study was conducted. Artificial noise has been generated to test the performance of the algorithm in different noise conditions. To explore the algorithm in real substation environments, an empirical study was performed in indoor and outdoor environments. Artificial PD signal is generated by using a high voltage partial discharge (HVPD) Pico Coulomb (pc) calibrator to perform the field trials at two different sites i.e., power network distribution centre (PNDC) at the University of Strathclyde and TATA Steel at Port Talbot, Wales. A specialised radiometer sensor is used to measure PD signals. Received signals from voltage levels are converted into power signals (dBm) as input to the location algorithm. Various sensors configurations in indoor and outdoor environments were used. The algorithm’s performance was evaluated based on four parameters which include, the estimated location, localization error, the path loss exponent (PLE) optimisation and the scalability. Simulation and experimental studies show that there is sufficient agreement and RSS based localization is a promising technique that can be used autonomously in future condition monitoring of HV systems on a continuous basis

An Efficient Algorithm for Partial Discharge Localization in High-Voltage Systems Using Received Signal Strength

The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms

Erfassung und Evaluierung von Teilentladungen in Leistungstransformatoren mit speziellen Sensoren und Diagnoseverfahren

Transformers are key elements of the power grid. Due to their importance and high initial cost, asset managers utilize monitoring and diagnostic tools to optimize their operation and extend their service life. The main objective of this thesis is to develop new methods in the field of monitoring and diagnosis of transformers in order to reduce maintenance costs and decrease the frequency of forced outages. For this purpose, two concepts are proposed. Small generator step-up transformers are essential in wind and photovoltaic parks. The first presented concept entails an online fault gas monitoring system for these transformers, specially hermetically-sealed transformers. The developed compact, maintenance-free and cost-effective monitoring system continuously tracks the level of the key leading indicators of transformer faults in the gas cushion. The second presented concept revolves around partial discharge (PD) assessment by the UHF measurement technique, which is based on capturing the electromagnetic (EM) waves emitted in case of PD in the insulation of a transformer. In this context, the complex EM system established when probes are introduced into the tank of a transformer and with PD as the excitation source is analyzed. Drawing on this foundation, a practical approach to the detection and classification of PD with the focus on the selection of the optimal frequency range for performing UHF measurements depending on the device under test is presented. The UHF measurement technique also offers the possibility of PD localization. Here, the determined arrival time (AT) of the captured signals is critical. A PD localization algorithm, based on a multi-data-set approach with a novel AT determination method, is proposed. The methods and algorithms proposed for the detection, classification and localization of PD are validated by means of practical experiments

Partial Discharge Detection and localization Using Software Defined Radio in the future smart grid

Partial discharge (PD) occurs if a high voltage is applied to insulation that contains voids. PD is one of the predominant factors to be controlled to ensure reliability and undisrupted functions of power generators, motors, Gas Insulated Switchgear (GIS) and grid connected power distribution equipment. PD can degrade insulation and if left untreated can cause catastrophic insulation failure. However, PD pulse monitoring and detection can save cost and life prior to plant failure. PD is detected using traditional methods such as galvanic contact methods or UHF PD detection methods. Recently, an alternative method for PD detection and monitoring using wireless technology has become possible. Software Defined Radio has opened new opportunities to detect and monitor PD activity. This research makes use of SDR technology for PD detection and monitoring. The main advantages of SDR technology are that it is cost-effective and it is relatively immune against environmental noise. This is because the noise at electrical power stations is from around a few KHz to a few MHz and this is well below the SDR frequency range and PD frequency band (50-800 MHz). However, noise or interference also exists in the PD frequency band. These interferences are narrow band and mainly from FM, TV broadcasting and mobile telephony signals whose frequencies are well known, thus these interferences can be possibly processed and removed. In this research two SDR products (Realtek software defined radio RTL-SDR/Universal software radio peripheral USRP N200) are used to detect PD signals emitted by a PD source that was located at a distance of 1 m in case of RTL-SDR device while in case of USRP N200 the PD source was located at a distance of 3 m. These PD signals once received by an SDR device are recorded and processed offline in order to localize the PD source. The detected PD signal was around 20 dB above background noise in case of the RTL-SDR device and 25 dB above background noise in case of using the USRP N200. Selecting the appropriate SDR device depends on factors such as high sensitivity and selectivity. Furthermore, although USRP N200 is more expensive than RTL-SDR dongles, USRP N200 was preferred over RTL-SDR as it demonstrates higher sensitivity and overall better results. PD detection using SDR devices was conducted in the frequency domain. These result were validated using a high-end costly device, i.e. spectrum analyzer. Generally, SDR devices demonstrate satisfactory results when compared to spectrum analyzers. Considering that spectrum analyzers cost around £10,000, while a USRP N200 SRD device costs less than £1000, SDR technology seems to be cost-effective. Following PD detection, PD localization was performed using USRP N200 results, and a localization algorithm based on Received Signal Strength (RSS) was adopted. The localization result was within a 1.3-meter accuracy and this can be considered as a relatively good result. In addition, and for the purpose of evaluating the proposed scheme, more experiments were conducted using another system that is based on radiometric sensors which is WSN PD system. The estimated error was 1m in case of using the SDR-USRP N200 system and 0.8 m in case of using the WSN PD system. Results of both systems were very satisfactory, although some results at the corners of the detection grid were not good and the error was higher than 3 meters due to the fact that the RSS algorithm performs poorly at corners. These experiments were used to validate both systems for PD detection and localization in industrial environments

Improving RF-based partial discharge localization via machine learning ensemble method

Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise

Partial Discharges Detection and Spatial Localization in High Power Transformers Using UHF Method

Disertační práce je zaměřena na návrh a experimentální ověření nové metody detekce částečných výbojů ve výkonových olejových transformátorech s důrazem na rozlišení původu měřeného signálu z vnější a vnitřní oblasti transformátorové nádoby. Detekce a prostorová lokalizace částečných výbojů využívá záznam elektromagnetického signálu v pásmu UHF (Ultra vysoké frekvence). UHF metoda detekce částečných výbojů, označovaná také jako UHF PD, je jednou z nejpokročilejších metod umožňující přesnou prostorovou lokalizaci pozice zdroje signálu. Těžiště práce leží ve vývoji technických a programových prostředků pro odlišení signálů částečných výbojů pocházejících z vnitřního prostoru transformátoru a rušivých signálů přicházejících z přípojných vedení. Navržená technická a programová řešení tvoří souhrnně diskriminační metodu. Přesná a opakovatelná diagnostika stavu transformátoru je zaručena dodržením metodického postupu měření a nastavení diagnostického systému. Funkčnost diskriminační metody byla ověřena při reálné diagnostice blokových transformátorů v JE Dukovany za provozu. Výstupem návrhu metodiky je vytvoření kalibračního postupu a následných procedur pro zajištění objektivních, opakovaných a porovnatelných měření pro účely pravidelné prediktivní údržby transformátorů.The thesis discusses the design and experimental verification of a new method for detecting partial discharges in oil-cooled high-power transformers, emphasizing the origin of the measured signal from the outer and inner regions of the transformer tank. The detection and spatial localization of partial discharges utilizes UHF (Ultra High Frequency) electromagnetic signal measurement. The UHF partial discharge detection method, also referred as UHF PD, is one of the most advanced techniques for the accurate spatial localization of the signal source position. The focus of the thesis lies in the development of technical and software solutions for the separation of partial discharge signals originating from the internal space of the transformer and the spurious signals from the connection lines. The proposed technical and program solutions constitute a signal discriminatory method. The precise and repeatable diagnostics of the transformer state are guaranteed by observing the measurement methodology and special setting of the diagnostic system. The functionality of the signal discriminatory method was verified during real measurement of the oil-cooled power transformers at the Dukovany nuclear power plant in operation. The output of the designed methodology is to set up a calibration procedure and follow-up steps to ensure objective, repeatable, and comparable measurements for the purposes of regular predictive maintenance in transformers.

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems