# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 1 Introduction  
As more and more distributed generation (DG) and electric vehicles are connected to the distribution networks, the operational characteristics of the distribution system have been changed [1]. The bidirectional power flow occurs when the generation from distributed units exceeds the local load, and the voltage profile of the distribution network will be influenced strongly. Furthermore, the network configuration of the smart distribution network will be changing dynamically to achieve minimum power loss and voltage deviations. The real-time monitoring of the distribution network is becoming more and more challenging due to the increasing dynamics and changing behavior of actors in distribution systems [2]. The operating condition needs to be perceived first and a key technology obtained to achieve the reliable and accurate system condition awareness is known as the distribution system state estimation (DSSE). The DSSE is used to calculate the real-time measurement data according to some optimal estimation criterion, thus improving the accuracy of the data and obtaining a credible state for the whole system. The traditional real-time measurement data in thedistribution system mainly comes from the supervisory control and data acquisition (SCADA) system, whose sampling period is 2-5 s. The three-phase voltage and power measurements are mainly configured at the high- and low-voltage sides of the distribution substation and some critical buses. Only the current amplitude measurements are configured at the sub section post (SSP), ring main unit (RMU), cable branch boxes, and column switches with no voltage and power measurements [3]. Different from the SCADA system, the sampling period of the phasor measurement units (PMUs) is 30-40 ms so that PMUs can capture the dynamic changes of the distribution system. PMUs can not only measure voltage phasors and current phasors accurately, but also provide synchronized real-time measurements (with reference to the global position system (GPS) time). In the transmission systems, the PMUs and wide area monitoring systems are applied to constantly monitor grid stability, detecting power faults, and supporting power system restoration. While in the distribution systems, with the development of an active distribution network and the effect of a bidirectional power flow, measurement instruments, such as PMUs, have become increasingly useful. The unbalanced and uncertain characteristics of the distribution network and the small angle differences between the bus voltage and line current phasors require higher phase measurement accuracy. As a result, PMUs are indispensable in distribution systems and one of the most known examples used in distribution systems is the micro-PMU [4]. Originally, the PMUs in the distribution system are mainly installed for the advanced applications, such as fault diagnosis and islanding detection, instead of the DSSE [5; 6]. Considering the economics and infrastructure of the distribution system, it is not necessary to configure the PMU at each bus. At present, the research on the configuration scheme of PMUs in the distribution system is mainly focused on the complete observability of the network, improvement of the state estimation accuracy and the observability of the system power flow [2; 4; 7; 8]. In the SCADA system, overhead lines, coaxial cables, and power line carriers are fundamental equipment to transmit measurement data, while in the PMUs, the wireless and wireline communication infrastructures, such as the Wi-Fi, Bluetooth, ZigBee, and fiber optics, are used to carry measurement information [9; 10; 11; 12]. In Reference [13], a Cloud-based architectural solution is proposed to decouple the handling of PMU measurement data from the underlying physical devices. In DSSE, the measurement data is treated as uncertainty data, and the uncertainty of PMU measurement data is smaller than that of SCADA measurement data. The state estimation is carried out by using probability statistics or fuzzy analysis [14; 15].