Masoud Farivar

Inverter VAR control for distribution systems with renewables

Motivated by the need to cope with rapid and random fluctuations of renewable generation, we presents a model that augments the traditional Volt/VAR control through switched controllers on a slow timescale with inverter control on a fast timescale. The optimization problem is generally nonconvex and therefore hard to solve. We propose a simple convex relaxation and prove that it is exact provided over-satisfaction of load is allowed. Hence Volt/VAR control over radial networks is efficiently solvable. Simulations of a real-world distribution circuit illustrates that the proposed inverter control achieves significant improvement over the IEEE 1547 standard in terms of power quality and power savings.

Optimal inverter VAR control in distribution systems with high PV penetration

The intent of the study detailed in this paper is to demonstrate the benefits of inverter var control on a fast timescale to mitigate rapid and large voltage fluctuations due to the high penetration of photovoltaic generation and the resulting reverse power flow. Our approach is to formulate the volt/var control as a radial optimal power flow (OPF) problem to minimize line losses and energy consumption, subject to constraints on voltage magnitudes. An efficient solution to the radial OPF problem is presented and used to study the structure of optimal inverter var injection and the net benefits, taking into account the additional cost of inverter losses when operating at non-unity power factor. This paper will illustrate how, depending on the circuit topology and its loading condition, the inverters optimal reactive power injection is not necessarily monotone with respect to their real power output. The results are demonstrated on a distribution feeder on the Southern California Edison system that has a very light load and a 5 MW photovoltaic (PV) system installed away from the substation.

Equilibrium and dynamics of local voltage control in distribution systems

We consider a class of local volt/var control schemes where the control decision on the reactive power at a bus depends only on the local bus voltage. These local algorithms form a feedback dynamical system and collectively determine the bus voltages of a power network. We show that the dynamical system has a unique equilibrium by interpreting the dynamics as a distributed algorithm for solving a certain convex optimization problem whose unique optimal point is the system equilibrium. Moreover, the objective function serves as a Lyapunov function implying global asymptotic stability of the equilibrium. The optimization based model does not only provide a way to characterize the equilibrium, but also suggests a principled way to engineer the control. We apply the results to study the parameter setting for the inverter-based volt/var control in the proposed IEEE 1547.8 standard.

Local voltage control in distribution systems: An incremental control algorithm

Inverter-based local volt/var control forms a closed-loop dynamical system whereby the measured voltage determines the reactive power injection, which in turn affects the voltage. There has been only a limited rigorous treatment of the equilibrium and dynamical properties of such feedback systems. In this paper, we expand on our prior result that reverse-engineers a class of non-incremental voltage control schemes and provides a principled way to rigorously engineer the control to incorporate new design goals and/or achieve better dynamical properties. Specifically, it has been observed in the literature that in practical circumstances the droop-based control scheme, a commonly adopted non-incremental voltage control, can lead to undesirable oscillatory behaviors even in the case of a single inverter unit. This motivates us to forward-engineer the local voltage control and apply the (sub)gradient method to design an incremental voltage control algorithm that demands less restrictive condition for convergence. We provide a sufficient condition to ensure convergence of the proposed control algorithm and evaluate its performance on a real-world distribution feeder in Southern California with multiple large PV generation units through simulations.

Pseudo-gradient based local voltage control in distribution networks

Voltage regulation is critical for distribution systems, and has become a much more challenging problem with the increasing proliferation of distributed renewable energy resources that cause frequent and rapid voltage fluctuations beyond what can be handled by the traditional voltage regulation methods. In this paper, motivated by the shortcomings of two previously proposed inverter-based local volt/var control algorithms, we design a pseudo-gradient based voltage control algorithm for the distribution network that does not constrain the allowable control functions while admits low implementation complexity. We characterize the convergence of the pseudo-gradient based control scheme, and compare it against the two previous algorithms in terms of the convergence condition and the convergence rate.

A 16-channel 1.1mm 2 implantable seizure control SoC with sub-μW/channel consumption and closed-loop stimulation in 0.18µm CMOS

We present a 16-channel seizure detection system-on-chip (SoC) with 0.92μW/channel power dissipation in a total area of 1.1mm2 including a closed-loop neural stimulator. A set of four features are extracted from the spatially filtered neural data to achieve a high detection accuracy at minimal hardware cost. The performance is demonstrated by early detection and termination of kainic acid-induced seizures in freely moving rats and by offline evaluation on human intracranial EEG (iEEG) data. Our design improves upon previous works by over 40× reduction in power-area product per channel. This improvement is a key step towards integration of larger arrays with higher spatiotemporal resolution to further boost the detection accuracy.

Hardware-friendly seizure detection with a boosted ensemble of shallow decision trees

Efficient on-chip learning is becoming an essential element of implantable biomedical devices. Despite a substantial literature on automated seizure detection algorithms, hardware-friendly implementation of such techniques is not sufficiently addressed. In this paper, we propose to employ a gradientboosted ensemble of decision trees to achieve a reasonable trade-off between detection accuracy and implementation cost. Combined with the proposed feature extraction model, we show that these classifiers quickly become competitive with more complex learning models previously proposed for hardware implementation, with only a small number of low-depth (d <; 4) “shallow” trees. The results are verified on more than 3460 hours of intracranial EEG data including 430 seizures from 27 patients with epilepsy.