Common Challenges in Voltage-Frequency Control Systems

🔋 In a microgrid, multiple distributed sources must proportionately share the load demand while simultaneously maintaining voltage and, in the case of AC microgrids, also frequency stability. Broadly, the approaches to address this challenge fall into two main categories: those that rely on communication links between the inverter modules and those that operate without communications, typically leveraging the droop concept. 🔌 Communication-based control generally offers excellent voltage regulation and proper power sharing, often without requiring secondary control. They achieve tight current sharing, high power quality, and fast transient response, while also reducing circulating currents. Their primary disadvantages include increased system cost due to the need for communication lines, which can also be susceptible to interference over long distances, thereby reducing system reliability and expandability. ⚡ Droop-based control methods tend to be cost-effective, more reliable, and easier to expand due to their plug-and-play capability, as they do not require communication links. Droop control inherently leads to frequency and voltage deviations and has a slow dynamic response. They can also cause circulating currents due to line impedance mismatches and perform poorly with fluctuating renewable energy sources. The key droop methods are: 1️⃣ Conventional Frequency/Voltage Droop Control: It is easy to implement and offers high expandability, modularity, and flexibility. Its drawbacks include being affected by physical parameters, resulting in poor voltage-frequency regulation, slow dynamic response, and poor harmonic sharing. 2️⃣ Virtual Structure-Based Methods: These are generally not affected by physical parameters and offer improved power-sharing performance and system stability. They can also handle linear/nonlinear loads and mitigate harmonic voltages. However, voltage regulation isn't always guaranteed, and they may require knowledge of physical parameters and low-bandwidth communication. 3️⃣ Construction-and-Compensation-Based Methods: These generally offer improved voltage regulation, system stability, and power sharing. They can reduce reactive power sharing errors and are often robust to communication delays. 4️⃣ Common Variable-Based Control Method: This approach achieves accurate proportional load sharing and is robust to system parameter variations, being unaffected by physical parameters. The main challenge is the difficulty in measuring the common bus voltage over long distances, and a common voltage may not exist in complex or distributed systems. #microgrids #powerelectronics #lvdc #renewables #cleanenergy #control

When Loads Move Faster Than the Grid Can Think NERC’s latest white paper doesn’t speculate. It documents. Emerging large loads, data centres, AI clusters, hydrogen, crypto, aren’t just big. They’re fast, invisible, and operating on their own timelines. ➤ A 450 MW data centre ramped down to 40 MW in 36 seconds. No fault. No command. No visibility. Just software doing what it was programmed to do. ➤ A 1,500 MW load drop in the Eastern Interconnection wasn’t a breaker trip. It was data centres transferring to backup after multiple voltage dips. The substations didn’t trip. The load simply left the grid. NERC’s Language Is Clear: • “System operators cannot account for the load response or create accurate forecasts.” • “Ramp rates of 1.9 p.u./sec over 250 ms.” • “Load ramping now challenges frequency regulation and reserve sufficiency.” Beyond Planning: The Real Risk Is Loss of Control This isn’t just about planning. It’s about control. And right now, control is slipping. The grid still assumes load is passive. It’s not. It’s power electronic, programmable, and often strategically opaque. The consequence? • Frequency spikes from loss of load, not generation. • Oscillations triggered by AI training cycles. • Generator instability from sudden reactive changes. • Load behaviour that mimics uncoordinated inverter-based generation. • UFLS failing, not because it tripped too late, but because the load was already gone. And We Haven’t Even Mentioned Restoration: Blackstart strategies now face an unmodeled threat 1) Large loads that reconnect too fast, or demand more than the island can handle. 2) Restoration isn’t just harder, it’s being shaped by load behaviour no one controls. Why the Old Interconnection Framework Doesn’t Hold Up: We’ve built interconnection frameworks around static MW thresholds. But none of them account for ramp speed, backup transfer logic hidden behind the meter, or autonomous disconnection outside system visibility. Yet these are now determining how the system fails, and how it recovers. Planning Means Nothing If Visibility Comes Too Late: i) Planning adequacy means nothing if a 300 MW electrolyser ramps to zero in 2 seconds because its own logic deems the voltage “unstable.” ii) Frequency control is irrelevant if the load that tripped wasn’t visible to begin with. iii) Restoration is compromised if blackstart islands can’t segment large loads in time. This is not a future scenario. It’s happening now. Quietly. Repeatedly. Systemically. #GridResilience #LargeLoads #NERC #DataCenters #AIInfrastructure #Hydrogen #FrequencyControl #VoltageStability #RampRates #DynamicLoads #InverterDominatedGrids #PowerSystemStability 

The Spanish government recently released its evaluation report investigating the causes of Europe's worst blackout on April 28, 2025. Unfortunately, mainstream narratives oversimplify the findings, potentially misleading the public about the actual root causes of this severe event. In science, we recognize the importance of avoiding the "fallacy of oversimplified cause," which involves wrongly attributing an event to a single factor while ignoring crucial underlying factors. Current media narratives highlight only voltage regulation issues while dismissing the essential role played by insufficient system inertia. Indeed, the official report clearly states that the blackout had a "multifactorial origin." My academic colleague in Spain, Luis Badesa, has provided important insights into this complexity. He hypothesized early on that the severe overvoltages initiating the blackout were triggered by control actions—specifically power system stabilizers (PSSs) and inverter-based controls—implemented to damp inter-area frequency oscillations. Additionally, reactive power management was significantly compromised. Renewable, cogeneration, and waste (RCW) power plants, operating in constant power factor mode, altered their reactive power outputs, exacerbating voltage oscillations during frequency disturbances preceding the initial generation losses. Badesa's preliminary analysis highlights critical questions about why these oscillations were insufficiently damped, suggesting their persistence directly impacted voltage control and system stability. He notes that overvoltages in southwest Spain were likely connected to these prior oscillations, describing the blackout vividly: “This wasn’t one failure. It was a cascade, like falling off a cliff, breaking a leg, and getting attacked by a bear.” Essentially, actions intended to stabilize frequency inadvertently undermined voltage regulation and reactive power management processes, causing severe overvoltages that set off a cascade of generation losses. Each disconnection worsened reactive power imbalances, amplifying voltage spikes and leading to more generator trips—culminating in a full-scale blackout. Understanding this complex chain of events is crucial. Oversimplifying the narrative risks obscuring critical lessons we must learn to build a more resilient power grid. For further insights: [1] https://lnkd.in/gcRSAHBM [2] https://lnkd.in/g2ew6JAr  [3] https://lnkd.in/gz555CqK

For most of the last century, generators stabilised the grid as a by-product of producing energy. Today, we are building assets that stabilise the grid without producing energy at all. That shift identifies the binding constraint. Electricity system transition is no longer constrained by renewable resource availability. It is constrained by deliverability and operability. In inverter-dominated systems under rapid load growth, the binding constraints are: - transmission and major substation capacity - system strength, fault levels, frequency and voltage control - connection and commissioning throughput - secure operation under worst-day conditions - execution pace across networks and system services Generation capacity remains necessary. On its own, it no longer delivers firm supply or supports large new loads. Historically, synchronous generators supplied energy and stability together. Inertia, fault current, voltage support, and controllability were implicit. As synchronous plant retires, these services must be provided explicitly. Stability shifts from physics-led to control-led. System behaviour becomes more sensitive to modelling accuracy, protection coordination, control settings, and real-time visibility. Curtailment is not excess energy. It is a deliverability or security constraint. When transmission and substations lag generation, congestion and curtailment rise. Independent analysis shows that delay increases prices and emissions by extending reliance on higher-cost thermal generation. Distribution networks are no longer passive. They now host distributed generation, storage, EV charging, and large loads at the edge of transmission. Voltage control, protection coordination, hosting capacity, and connection throughput now constrain both decarbonisation and industrial growth. Firming is a hard requirement. Batteries provide fast frequency response and contingency arrest. They do not provide multi-day energy and do not replace networks or system strength in weak grids. Demand response reduces peaks. It cannot be relied upon for system-wide security under stress. Execution speed is critical. Slow delivery increases congestion duration, curtailment exposure, reserve requirements, and reliance on ageing plant. These effects flow directly into costs, emissions, and reliability. This is why electricity bills can rise even when average wholesale prices fall. Costs are driven by peak demand, contingencies, and security, not average energy. Large digital and industrial loads are transmission-scale, continuous, and failure-intolerant. They increase contingency size and correlation risk. At that scale, loads do not connect to the grid, they shape it. Supporting growth requires time-to-power, transmission and substation capacity in load corridors, explicit system strength and fault levels, operable firming under worst-day conditions, scalable connection and commissioning, and early procurement of long lead time HV equipment. #energy

Inverter setting mismatch triggers 1GW HVDC outage between Estonia and Finland In January 2026, a technical incident occurred in Estonia during testing of the new 100MW Hertz 1 (Kiisa) battery energy storage system (BESS). The event triggered protective relays, resulting in the emergency shutdown of over 1GW of HVDC capacity, specifically the EstLink 1 and EstLink 2 interconnectors. The root cause was an incorrect parameter configuration in the BESS Nidec Conversion grid-forming inverters, which induced low-frequency network oscillations. The feedback gains within the Virtual Synchronous Machine control algorithms were set with excessive sensitivity, effectively amplifying rather than damping the oscillations. I recall similar experiments in Matlab while modeling excitation controllers for synchronous machines during my diploma project. Back then, the physical inertia of real synchronous machines limited the visibility of such transients on a gigawatt scale. Now, with the rise of high-capacity inverter-based resources, these scenarios have become a physical reality. The issue has already been resolved by increasing response delays and tuning down the feedback gain coefficients. However, this case is the first practical demonstration I have witnessed of a well-known theoretical vulnerability: the susceptibility of inverter-dominated grids to cyber threats. While this specific incident was not a cyberattack but a standard test on high-power equipment, it highlights a critical risk. A simple modification of feedback gains in inverter control loops can lead to massive grid instability. BESS invertors requires the same level of protection and rigorous safety standards as nuclear or aviation control systems. And not only utility-scale BESS, but all grid-forming units connected to the entso-e grid. What was once a theoretical concern has now been proven in practice. #BESS #HVDC

Voltage Control Impact on Grid-Forming Inverter Stability --------------------------------------------------------- As power systems transition toward higher shares of inverter-based resources (IBRs), grid-forming inverters (GFMIs) are becoming essential for stability in low-inertia grids. Unlike grid-following converters, GFMIs can autonomously establish voltage and frequency. However, the flexible control architecture of GFMIs enables multiple voltage control strategies, raising the question of how they affect system stability and dynamic performance under varying grid strength conditions. Our recent conference paper investigates three voltage control strategies for GFMIs: • Fixed Voltage Control (FVC) • Primary Voltage Control (PVC) • Automatic Voltage Regulation (AVR) Using frequency-domain analysis (Bode and Nyquist plots) and EMT time-domain simulations in MATLAB/Simulink, we evaluate the small-signal stability of these strategies under different grid strength scenarios. Key findings include: • All strategies show similar performance under weak grid conditions. • Stability behaviour diverges as grid strength increases. • FVC demonstrates the highest stability margins across grid strengths. • AVR may trigger low-frequency oscillations in strong grids, showing reduced robustness at high SCRs and the need for enhanced control approaches.. For more information: 📘 Paper Title: Impact of Different Voltage Control Strategies on Small-Signal Stability of Grid-Forming Inverters ✍️ Authors: Nabil Mohammed, Md Rakibuzzaman Shah, Nima Amjady 📍 Conference: IEEE International Conference on Energy Technologies for Future Grids (ETFG) 🔗 Links : https://lnkd.in/gfpQYkWG ; https://lnkd.in/gkjP2EY3 Special thanks and acknowledgment to CSIRO for supporting this research as part of the Australian Research in Power Systems Transition (AR-PST), Stage 5, Topic 2 (Stability Tools and Methods). #GridFormingInverters #VoltageControl #PowerElectronics #SmartGrids #PowerSystemStability #RenewableIntegration #FutureGrids #EnergyTransition 

🌀 Why Electric Motors Fail on Cheap VFDs By Roger Fritz – 45 Years in Industrial Automation https://lnkd.in/gaPhzEMb Electric motors are the backbone of modern industry. From HVAC systems to conveyor lines, pumps to fans, they drive productivity across every sector. But in recent years, I’ve seen a troubling trend: motors failing prematurely—not because of mechanical wear, but because of poor-quality Variable Frequency Drives (VFDs). After 45 years in the field and over 100,000 VFD installations, I can tell you exactly why. ⚠️ The Hidden Cost of Cheap VFDs Budget VFDs may look appealing on paper, but they often compromise on the very parameters that protect motor life. Here’s what’s going wrong: 1. Dirty Power Output: Harmonics and Voltage Spikes Cheap drives often lack proper filtering and output stage design. The result? - High harmonic distortion - Voltage spikes that exceed motor insulation ratings - Unstable waveform profiles that cause overheating and winding stress These issues silently degrade motor windings, leading to insulation breakdown and eventual failure. 2. Carrier Frequency Chaos Carrier frequency—the switching rate of the drive’s output transistors—matters more than most realize. - Low-quality drives often default to high carrier frequencies to reduce audible noise, but this increases motor heating. - Others use erratic or unstable carrier frequencies, causing torque ripple and vibration. Without proper tuning, motors run hot, noisy, and inefficient—especially in constant torque applications. 3. Poor Voltage and Current Regulation Precision matters. Cheap VFDs often fail to regulate voltage and current under load, leading to: - Voltage imbalance across phases - Current spikes during acceleration or deceleration - Inconsistent torque delivery This not only stresses the motor but wreaks havoc on connected mechanical systems. 4. Lack of Motor Protection Features Premium drives offer built-in protections: - Thermal modeling - Ground fault detection - Phase loss and imbalance alarms Budget drives? Often none of the above. Motors are left vulnerable to faults that could have been prevented. 🛠️ Real-World Consequences I’ve seen motors fail in under six months when paired with bargain-bin drives—especially in demanding environments like sawmills, water treatment plants, and packaging lines. The cost of downtime, replacement, and lost productivity far outweighs the savings on the drive. ✅ What to Look For If you're specifying VFDs, here’s what I recommend: - True sine wave output or advanced filtering - Carrier frequency tuning matched to motor type and application - Voltage and current regulation under dynamic load - Built-in motor protection features - Brand reputation and field-tested reliability 🧭 https://lnkd.in/gaPhzEMb #Applications #DCMotors #ACMotors #MotorControlSolutions #Reach #Automation #EnergyManagement #RefrigerationSystems #HeavyIndustry #ACDCHotline #DriveSolutions

⚡ Why Grid-Forming Inverters Don’t Shine in Strong Grids With the rapid rise of inverter-based resources, grid-forming (GFM) inverters are often described as the future of power systems. They promise virtual inertia, voltage control, black-start capability, and system strength. Yet, a question keeps coming up in real projects and studies: 👉 Why do grid-forming inverters often appear to perform worse than grid-following (GFL) inverters when connected to a strong grid? The answer lies not in technology limitations, but in control philosophy mismatch. A strong grid — characterized by high short-circuit ratio (SCR) and low Thevenin impedance — already has tightly regulated voltage and frequency, usually dominated by synchronous machines. In such a system, the grid does not need another device trying to establish voltage and frequency. And that’s exactly what a GFM inverter is designed to do. Grid-forming inverters behave like controlled voltage sources. They regulate voltage magnitude and frequency using droop, virtual inertia, and virtual impedance. These mechanisms work beautifully in weak grids, islanded systems, or black-start scenarios, where the inverter must create grid strength. But in a strong grid, voltage and frequency hardly move. Frequency deviations are tiny, voltage stiffness is high, and impedance is very low. As a result, the droop control of a GFM inverter receives almost no usable signal. Large power commands lead to minimal voltage or frequency change, forcing the inverter toward current limits without producing meaningful system impact. What looks like “poor performance” is actually the inverter respecting its protection and control limits. Grid-following inverters, on the other hand, are optimized for exactly this environment. They assume a stiff voltage source exists. Using a PLL, they lock onto a clean, strong grid waveform and inject controlled current. In strong grids, PLLs are stable, current controllers are fast, and power injection is accurate. The grid does the hard work; the inverter simply follows. Another practical issue appears during disturbances. In strong grids, fault current demand is high. A GFM inverter must protect itself while attempting to maintain voltage, which often results in aggressive current limiting and local voltage depression. GFL inverters naturally reduce current injection during faults, making them appear calmer and more predictable in system studies. This leads to an important realization: Grid-forming inverters are not designed to outperform grid-following inverters in strong grids. That’s why the industry is moving toward adaptive and hybrid control strategies. In strong grids, inverters behave in a grid-following manner. As the grid weakens — due to high renewable penetration, outages, or islanding — they smoothly transition into grid-forming mode and start providing voltage, frequency, and inertia support.

PVT variations- 1) Process (P) • Process variation = run-to-run, die-to-die and within-die (local) variations in device geometry, doping, oxide thickness • Geometrical variations (L, W): up to ~±2–10% depending on node and feature (patterning, OPC). • Threshold voltage (Vth) / drive current (Ion): variability can be up to ~±5–10% Effect - • Delay spread, timing failures, SRAM stability (Vmin), increased leakage (for some corners), lower yield. • Within-die mismatch affects analog matching, SRAM bitcell failure, and critical paths. Mitigation- 1. Statistical timing + variation-aware sign-off (Monte-Carlo, SSTA) — design to statistical yield 2. Adaptive Body Bias (ABB) / Static Body Bias (SBB) — shift Vth per-die or per-block to recover speed or cut leakage. 3. Design margins & conservative corners — guardbanding 4. Sizing & redundancy — upsizing transistors on critical paths; spare rows/columns and ECC for memories. 5. Layout techniques for matching — common-centroid, interdigitation, dummy fingers 6. Process control & calibration — on-chip sensors (ring oscillators, corner detectors) + post-silicon calibration (voltage trim). 7. Variation-tolerant circuit styles — error detection/recovery , differential signaling 2) Voltage (V) • (I/O, analog) ±5%; core rails ~±1–3% . Transient droops during switching can be (tens of mV). • Transient droop (IR drop + decoupling limits) can cause VDD reductions of several % to >10% Effect- • Delay is sensitive to VDD near Vth: small % change in VDD → larger % change in delay. • Lower VDD increases delay and higher VDD increases leakage and stress. Mitigation- 1. Robust power-grid & decoupling 2. Fast local regulators / LDOs / point-of-load converters 3. Dynamic Voltage and Frequency Scaling (DVFS) with margining 4. OCV (on-chip variation) and timing monitors (Razor, canaries) that trigger corrective action (voltage bump or clock slow-down). 5. Power aware synthesis / floorplanning 3) Temperature (T) • Chips operation-consumer ~−40°C to +85°C; industrial/automotive up to +125°C or more. On-chip hotspot delta from ambient can be 20–60°C • parameters (mobility, leakage, bandgap) depend on T — mobility decreases with increasing T (leakage/subthreshold current increases with T. Mobility and resistivity changes are of a few % to tens of % Effect - • higher T → slower carrier mobility → longer delay, but there are cases of temperature inversion (delay decreases with temperature in some corners near threshold because Vth shifts dominate). Leakage increases strongly with T (exponential). • Large ΔT across chip causes frequency variations and potential hot-spot induced failures. Mitigation- 1. Thermal management — heat sinks, active cooling, airflow, PCB thermal vias. 2. On-chip temperature sensors & dynamic thermal management (DTM) — throttle frequency, migrate workload, DVFS 3. Place sensitive circuits away from hot blocks 4. Worst-case sign-off + silicon monitoring

We are installing BESS faster than we are learning how to operate them. That’s dangerous. Everyone talks about MW and MWh. Almost no one talks about how the BESS actually behaves when the grid is stressed. That’s the real problem. A Battery Energy Storage System is not a big power bank. It is a grid-active machine. And the wrong control philosophy can quietly turn a “grid support asset” into a grid destabilizer. Following up on my previous post about the coming BESS protection crisis, control modes are the next blind spot no one wants to admit. PQ Mode — The Comfortable Default • Fixed active and reactive power setpoints • Pure grid-following behavior • Zero inertia contribution Great for: – Energy shifting – Peak shaving – Load smoothing But let’s be honest: PQ mode assumes the grid is strong, stiff, and forgiving. In real disturbances? PQ doesn’t help. It waits. The grid leads. The BESS follows. VSG Mode — The Uncomfortable Reality • Emulates inertia and damping • Actively stabilizes frequency and voltage • Can operate in weak or islanded systems • Enables grid-forming and black start This is not “advanced control.” This is what replacing synchronous machines actually requires. The BESS leads. The grid follows. Why this is becoming critical Renewables didn’t just change generation. They changed grid physics. • Mechanical inertia is disappearing • Frequency events are faster than protection can react • Weak grids are no longer edge cases—they are becoming standard And yet… We keep deploying BESS in PQ mode by default because it’s cheaper, familiar, and easier to interconnect. That’s how fragile grids are born. PQ vs VSG is NOT a preference It is a design decision with system-wide consequences. • Strong grids → PQ may survive • Weak grids → PQ can amplify instability • Future grids → hybrid or grid-forming control is unavoidable This is not about control philosophy. It is about whether the grid has a leader during a disturbance. Hard truth Treating BESS as plug-and-play storage is one of the fastest ways to create: • Protection miscoordination • Frequency collapse scenarios • “Mysterious” trips no one predicted Control mode selection belongs at the same table as: protection studies, SCR assessment, fault ride-through, and system stability. Not as an afterthought. Not as a checkbox. BESS is not about energy. It is about control, stability, and responsibility. Real question for the industry: On your projects— Are control modes being selected based on system strength and stability studies… Or are we still optimizing for minimum compliance and lowest CAPEX, hoping the grid will figure out the rest? Engineers, operators, planners—what are you actually seeing in the field? Hanane Oudli🌍