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Abstract-This paper demonstrates a concept to detect bad data in state estimation when the leverage measurements are tampered with gross error. The concept is based on separating leverage measurements from non-leverage measurements by a technique called diagnostic robust generalized potential (DRGP), which also takes care of the masking or swamping effect, if any. The methodology then detects the erroneous measurements from the generalized studentized residuals (GSR). The effectiveness of the method is validated with a small illustrative example, standard IEEE 14-bus and 123-bus unbalanced network models and compared with the existing methods. The method is demonstrated to be potentially very useful to detect attacks in smart power grid targeting leverage points in the system. Index Terms-distribution management system (DMS), remote terminal unit (RTU), state estimation (SE), leverage measurements, bad data detection (BDD), generalized studentized residuals (GSR), diagnostic-robust generalized potentials (DRGP)

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This paper demonstrates a concept to detect bad data in state estimation when the leverage measurements are tampered with gross error. The concept is based on separating leverage measurements from non-leverage measurements by a technique called diagnostic robust generalized potential (DRGP), which also takes care of the masking or swamping effect, if any. The methodology then detects the erroneous measurements from the generalized studentized residuals (GSR). The effectiveness of the method is validated with a small illustrative example, standard IEEE 14-bus and 123-bus unbalanced network models and compared with the existing methods. The method is demonstrated to be potentially very useful to detect attacks in smart power grid targeting leverage points in the system

Abstract-State estimation has become an important task in modern energy/ distribution management systems. However, the state estimation is not very popular in modern unbalanced threephase distribution systems. This paper proposes a method for three-phase state estimation with detailed three-phase modelling of system components including switches and star and delta connected loads. This method is then tested on a standard IEEE 13-bus system and the results are compared with load flow results. Keywords-state estimation, distribution management systems (DMS), three-phase modelling, weighted least squares (WLS), switch modellingI. INTRODUCTION With the influx of phasor measurement units (PMUs), intelligent metering etc. in transmission systems and smart meters with information and communication technology (ICT) infrastructure in distribution systems, power systems now-adays need to be monitored and controlled efficiently [1]. To enable this, the states of the system need to be observed properly. This would help to influence the operational decisions and thus, to avoid contingency and cascaded tripping. It is done through an energy/ distribution management system (EMS/DMS) function-the state estimation (SE) [2], [3]. This function estimates the bus voltages and angles based on the available measurements, network data and topology information. Figure I shows a typical DMS architecture.In transmission systems, the state estimation concept is well established but in distribution systems due to the absence of sufficient measurements and unbalanced and asymmetric nature of the system, it was not mandatory to have a state estimation function. But with growing number of controllable devices and the incorporation of smart meters in the system, state estimation is becoming important in distribution network operation.As a starting point, the solution methodology mainly focuses on weighted least squares (WLS) estimation technique [4]. But the majority of distribution systems operate under varying degrees of unbalance. Moreover, unlike the transmission system, the distribution system is radial in nature and has a higher R/X ratio. Therefore, the fast decoupled method causes numerical instability when applied to distribution systems [5]. Hence, this has paved the way for the need