This paper presents a sequential fault detection and identification algorithm for detecting bias in the measurements of partial discharge (PD) transformer insulation system using acoustic signals. The identifies any ultrasonic sensors by computing probability having that given carefully constructed measurement residual. residual is function noise possible fault. A set hypotheses assumed initially equal alarm probability. It only one sensor will acquire at time. Once hypothesis approaches 1,...

In this brief, a high-accuracy inertial navigation system (INS) for pipeline inspection gauge (PIG) is proposed. Two mechanization approaches are investigated. First, full INS dynamic model used, and second, 3-D reduced sensor (RISS) utilized. The uses the measurement unit (IMU) data to calculate solution, whereas RISS an encoder, one single-axis gyroscope, two accelerometers. proposed in brief its better accuracy less computational complexity. Due accumulated error or extended Kalman filter...

This paper proposes a novel invariant extended Kalman filter (IEKF), recently modified version of the (EKF), to estimate partial discharge (PD) location in transformer insulation system model. An acoustic signal measurement is utilized localize PD location. Unlike conventional EKF methods, where correction term used update state linearly proportional output error, proposed algorithm independent resulting fast response with significant reduction estimation error. In contrast EKF, method can...

This paper proposes an enhanced fusion technique to improve the accuracy of state estimation a navigational system. The Smooth Variable Structure Filter (SVSF) is examined estimate system's under model uncertainty. Its combination with Unscented Kalman (UKF) acquire better while being robust modeling uncertainty investigated. proposed hybrid method compared extended filter (EKF), UKF, and SVSF. algorithms fuse inertial measurement unit (IMU) Global Positioning System (GPS) measurements...

In this paper, high-accuracy estimation of partial discharge (PD) location in an oil insulation system is addressed. The study aims at forming a fault-tolerant PD localization system. Initially, the algorithm periodically and probabilistically checks for possible bias acoustic emission sensors' measurements. Once detected removed from measurements, multiple-model extended Kalman filters are used to estimate PD. proposed approach intended compensate increased measurement noise statistics that...

This article demonstrates the use of five different methods to estimate partial discharge (PD) location in an oil insulation system from noisy measurements. The measurements are obtained three ultrasonic sensors located places. map PD utilizing a nonlinear model. estimation techniques used this extended Kalman filter (EKF), unscented (UKF), smooth variable structure (SVSF), EK-SVSF, and UK-SVSF. last two filters combination EKF or UKF with SVSF, respectively, consider possible model...

Two adaptive trajectory tracking controllers for wheeled mobile robots are tested in this work. Adaptively tuned proportional control is one approach, where as the other controller uses a Universal Adaptive Stabilization (UAS) based technique. Using simulations, robustness of above quantified presence measurement noise. The measured terms Integral absolute magnitude error (IAE), square (ISE), and time multiplied by value (ITAE) criteria. It observed that UAS technique shows fast convergence...

In this article, a novel algorithm for high-accuracy partial discharge (PD) localization in an oil insulation system is proposed. This study aims to identify the statistics of dynamics and measurement noise sequences PD employ that information realizing better estimates. An extended Kalman filter (EKF) used estimate location. The performance enhanced by identifying true using maximum likelihood estimation approach. accuracy proposed optimal verified experimentally estimating location under...

This paper proposes a bias detection method in the voltage measurement of lithium-ion (Li-ion) battery cells to identify faulty sensor(s). The proposed is based on Bayesian probabilistic approach that detects possible any cell real-time. A hypothesis bank constructed for magnitudes each cell. Subsequently, fault algorithm computes probability associated with all hypotheses. Once certain converges unity, sensor and its are identified. quantified can then be compensated model Details followed...

We present a new method to accurately locate partial discharge by using sequential fault detection and identification (FDI) algorithm for detecting bias in the measurements of transformer insulation system acoustic signals. In this paper, novel technique is proposed identify possibility measurement errors generated from emission sensors during localization inside tank. The probabilistically detects identifies possible on sensors' measurement. This possibly caused sensor's fault, aging, or...